# A Comprehensive Survey on Machine Learning-Based Intrusion Detection Systems for Secure Communication in Internet of Things

Authors: S. V. N. Santhosh Kumar; M. Selvi; A. Kannan
Journal: Computational Intelligence and Neuroscience
 (2023)

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## Abstract

The Internet of Things (IoT) is a distributed system which is made up of the connections of smart objects (things) that can continuously sense the events in their sensing domain and transmit the data via the Internet. IoT is considered as the next revolution of the Internet since it has provided vast improvements in day-to-day activities of humans including the provision of efficient healthcare services and development of smart cities and intelligent transport systems. The IoT environment, by the application of suitable security mechanisms through efficient security management techniques, intrusion detection systems provide a wall of defence against the attacks on the Internet and on the devices connected with Internet by effective monitoring of the Internet traffic. Therefore, the intrusion detection system (IDS) is a resolution proposed by the researchers to monitor and secure the IoT communication. In this work, a meticulous analysis of the security of IoT networks based on quality-of-service metrics is performed for deploying intrusion detection systems by carrying out experiments on secured communication and measuring the network’s performance based on comparing them with the existing security metrics. Finally, we propose a new and effective IDS using a deep learning-based classification approach, namely, fuzzy CNN, for improving the security of communication. The major and foremost advantages of this system include an upsurge in detection accuracy, the accurate detection of denial of service (DoS) attacks more efficiently, and the reduction of false positive rates.


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## Body

## 1. Introduction

The Internet of Things (IoT) is the next revolution of the Internet, where the smart objects are connected and managed using remote way through the Internet [1] as a backbone. Events in the deployed IoT environment are sensed by the devices [2]. Due to the resource restraint nature [3] of sensors in the IoT and the environment where they are deployed, the provision of efficient security is becoming a major challenge [4]. The deployment environment of IoT devices is susceptible to several types of attacks [5] by intruders such as hackers, malicious software, and viruses [6]. The main aim of these intruders is to launch various forms of attacks which lead to the breach of data integrity in the network [7]. Moreover, the intruder can launch a denial of service (DoS) attack [8] which can exhaust both the network and device resources [9] such as energy in the IoT environment. From the literature, we see that security in IoT is provided in many works by using cryptography-based security mechanisms [10] such as symmetric key cryptosystems and public key cryptosystems [11].Since the IoT devices are resource inhibited, the use of cryptographic techniques in IoT security [12] results in significant communication and computation overhead. The design and deployment of intrusion detection systems (IDSs) are able to solve this issue. Therefore, IDSs have been used widely for monitoring and noticing impostors in IoT environments in order to provide efficient security in IoT communication [13, 14]. An IDS is a software which is running on the devices of IoT. The IDS monitors the behaviour of the devices in the network and identifies the malicious activities, if any, that are carried out by the devices. When the intrusion (attack) is detected [15], it informs the device administrator, who takes the necessary actions to prevent such intrusions by isolating the malicious devices. In this way, the IDSs are useful to ensure device security in the IoT environment [16]. Intrusion detection systems are broadly categorized into two classes [17] depending on the type of intrusions explicitly, anomaly-based intrusions and fraud-based intrusions. The anomaly intrusions are carried out by the external attackers. On the other hand, misuse intrusions are carried out by the internal members of the IoT system who are provided with security credentials by the system administrator.In another categorization performed depending on the type of intrusions, the IDSs are categorised into four, namely, IDS based on signature, IDS based on anomaly [18], IDS based on specification [19, 20], and hybrid approach-based IDS [21]. In IDS based on signatures [22], for each attack to be detected, it describes the attack pattern. In this scheme, a trigger message is raised when the attack matches the described pattern. By using this type of IDS, the known types of attacks can be detected more efficiently. The next category is anomaly-based IDS. In this scheme, data about the normal behaviour of the devices are obtained and values are set. If the device behaviour values, the IDSs consider it as suspicious behaviour [23, 24].The anomaly IDS is making use of the location and temporal constraints to identify the malicious devices. Different types of attacks are in communication such as DoS attacks [25–27], Sybil attacks [28, 29], selective forwarding attacks [30–32], worm whole attacks [33], black hole attacks [34–36], sink hole attacks [37–39], jamming attacks, and false data injection attacks [40]. Therefore, many investigators paid their thoughts in the detection of one or more of these attacks by proposing different methods for intrusion detection and prevention. However, the user community is interested in knowing the most suitable method from all these methods for protecting their IoT environment, as there are no standard guidelines and suggestions provided in the literature for choosing the most suitable approach for intrusion discovery and inhibition for IoT applications. The current need of the IoT community is the availability of a single work that analyses all the prevailing IDSs for IoT and affords suitable guidelines and recommendations for selecting the best scheme for safeguarding their application.Measuring the QoS is an imperative and challenging task. Many research studies used the false positive rate as an important metric for measuring the amount of security provided by IDSs. However, the attackers are carrying out the attacks to reduce the network performance and to consume the network resources for denying the opportunities to legitimate users from network access. This can be reduced more effectively by measuring the network capabilities and the service provided by the IoT. The measurement can be more efficient if and only if suitable metrics such as packet delivery ratio, delay, energy consumed, packets expected and accelerated by the nodes, and the overall network throughput are used for effective measurement of QoS and also for comparative analysis.Intrusion detection systems (IDSs) are powerful mechanisms [41] used on the Internet to identify the anomalous behaviour of attackers based on their malicious activities. In this scenario, the IDSs must be developed not only to detect the known types of malicious attacks but also to detect the new types of attacks carried out on the data communicated through the Internet using different approaches. Therefore, both the IDSs that are either existing in the literature or that are being proposed newly must be evaluated for their capabilities with suitable metrics such as detection accuracy, false positive rate, and error rate. A realistic IDS evaluation needs to be tested with both benchmark datasets and also real datasets. Here, the benchmarking datasets are the most important basis since such datasets are created using large amounts of network data by using efficient construction methods and tested with real systems for statistical significance, fair comparison, and validation of computational methods. When a newly proposed machine learning algorithm is evaluated with the given dataset, the capability of the detection algorithm is demonstrated with high accuracy. Such algorithms are tested with real network environment also; they provide a similar performance. Therefore, it is necessary to test a new classification algorithm not only by varying the number of features but also by evaluating them using benchmark datasets.The first dataset used in the evaluation of IDSs is the DARPA KDD 98 dataset. This was later extended to form the KDD’99 Cup dataset [42, 43]. This dataset consists of connection records that were created by considering various combinations of attack types and also by including the normal class, and they were used as the benchmark dataset for evaluating any network-based IDS. This dataset was developed by them by collecting and establishing their handmade and diverse test bed consisting of honeypots. Other types of datasets that were created for the effective evaluation of IDSs include the ISCXIDS 2012 dataset called the intrusion detection evaluation dataset proposed by Shivari et al. [44, 45], the realistic dataset generated by Haider et al. [46, 47], which was developed and validated using fuzzy rules, and finally the cloud intrusion detection dataset (CIDD) developed in 2017 by the Canadian Institute for Cyber Security [48].In this work, the evaluations were carried out uniformly using the benchmark dataset KDD’99 Cup dataset which is discussed in [43, 49], and also, the works were validated using real network trace data. One of the major reasons for selecting the KDD Cup 99 dataset for evaluation of the IDS systems is that it was the widely accepted benchmark dataset used in most of the research works on IDS. This dataset consists of 41 attributes in which 38 are numeric attributes and the other 3 are symbolic attributes. Some of the attributes of this dataset are the duration attribute that describes the time duration (number of seconds), the network connection duration attribute which is a continuous data attribute, and protocol_type (A discrete data attribute describing the transport and network layer protocols) including TCP and UDP.In this paper, a survey of IDSs developed for securing the Internet communication is presented, discussed, and compared. The major contribution of this paper is that it not only proposes a new intelligent IDS but also provides a comprehensive survey and comparative analysis of intrusion detection systems present in the literature and hence suggests suitable methods and works that can increase the security of IoT networks. IoT attacks originate from the Internet, and therefore, this work includes attacks on computer networks, including both wired and wireless networks with and without mobility [50]. It considers both acknowledgement-based schemes and machine learning-based intelligent approaches that are used to increase the security of the devices.The most important contribution of this paper is that it proposes a new intelligent IDS by extending the convolution neural network (CNN) with fuzzy rules for accurate decision making [51, 52]. Moreover, this work evaluates the existing IDSs that are also using fuzzy variables and compares them and detection time by employing suitable evaluation metrics such as false positive rate, energy consumed, packet delivery ratio, delay analysis, network throughput, and error rate in the IoT environment. Finally, it provides recommendations for choosing the best methods for designing and prevention, which are demonstrated through measurements and metrics, as well as the new IDS proposed in this work for IoT networks.


## 2. Literature Survey

Security of communication can be provided by using various methods including access control [53], optimization-based secure routing techniques [54], agent-based methods [55], temporal analysis [56], intrusion detection techniques developed for feature selection and classification, key management techniques, encryption and decryption methods, trust management techniques, firewalls, and application considerations [57–65]. Various authors have proposed a variety of mechanisms for providing security in IoT environments through IDS. In this section, the review of articles by various authors on IDS in the IoT has been provided with suitable analysis to highlight their benefits and limits in the field of IDS in the IoT [66–70]. Owais et al. [71] suggested a genetic algorithm-based [72] IDS for IoT. Moreover, their proposed algorithm generates intelligent rules for analysing the behaviour of the connected devices, so that it is possible to filter the malicious contents and also identify the malicious links present in the connected devices. The limitation of this work is that it has significant computation overhead and unknown attacks are not detected by this system accurately. Wang et al. [73] proposed the use of the hidden Markov model (HMM) for developing IDS. In their work, they considered efficiency, speed, and precision as the optimized parameters for evaluating their IDS. Their proposed technique is to detect the intrusions based on anomalous behaviour. Even though this system uses a statistical approach to handle novel situations, the detection accuracy is not uniform and hence lacks in security provision. Helai [74] suggested a signature-based IDS by applying data mining techniques. In this system, classification and pattern recognition methods have been employed to make a distinction on the normal behaviour from the abnormal behaviour of the devices, and hence, it improved the detection accuracy. However, this system is not suitable for an IoT environment since it involves more computational overhead.Kolias et al. [75] proposed a security system which uses swarm intelligence in the development of the IDS. The parallel nature of SWAM intelligence has decreased the training time and hence improved the quality-of-intrusion detection in many situations. However, the behaviour of the rules generated in this system was not uniform, and a small change in one rule affected the others. Jaisankar et al. [76] carried out an investigation to IDS. In this work, they used fuzzy rough sets [77] for performing outlier detection-based intrusion detection. Gendreu and Moorman [78] have carried out a survey of IDS developed in the past for providing security to IoT. In this survey, the authors highlighted the general process of IDS and the current research challenges of IDS in the IoT. Moreover, the requirements for developing the quality IDS in an IoT environment are explained in this work. However, the latest trends and attack patterns necessitate further analysis, and new techniques are necessary.Thangaramya et al. [79] proposed a secured model for outlier detection for wireless sensor networks. This article highlighted the open research challenges and provided the scope for future improvements in the methods used in the development of security in communication. Ammar et al. [80] explained the need for enhancing the security of IoT by providing security solutions for IoT. This work highlighted the basic idea for developing third-party security applications. Yang et al. [81] proposed a security model that analysed the security and privacy issues of the IoT. In this work, they have analysed the security issues on four layers. However, the detection and prevention measures for the attacks occurring in various layers are not provided in this work. Kouicem et al. [69] provided an inclusive investigation on reliability issues in the IoT. They explained the use of SDN since it is able to preserve both security and privacy more efficiently. However, they have observed that most of the existing approaches involve more computational complexities and overheads.From the related works, the research gaps identified are that most of the prevailing IDSs presented in the literature are generic in nature, focusing on network security, and most of them do not focus on the use of deep learning-based computational intelligence for developing a reliable intrusion detection system. Therefore, they are not appropriate for providing efficient security in the IoT environment. The need of the current IoT communication is the provision of a flexible and more efficient security mechanism that can find the known as well as novel types of attacks and prevent them more intelligently using artificial intelligence (AI) and machine learning (ML) techniques [82]. In this work, we propose a new intelligent IDS by performing feature extraction, feature selection, and intelligent classification by extending the CNN classifier with a fuzzy rule-based approach in which the fuzzy inference system identifies the intruders more effectively through efficient rule matching and also by performing deductive inference. Moreover, a comprehensive survey of IDSs in the IoT is also carried out in this work, which highlights the advantages and limitations of the prevailing IDSs available for the IoT environment and compares them with the proposed work. The major contributions of this work include the comprehensive literature survey, the identification of suitable metrics for comparison, the measurement of various parameters more efficiently by identifying the granularity of the measurement, and finally the proposal of a new IDS using deep learning techniques. Based on the experiments carried out in this work, it is found that the proposed intelligent IDS is more effective in terms of intrusion detection rates and also in the reduction of false positive rates.


## 3. Intrusion Detection Systems for IoT Communication

The IDS in IoT is classified into three groups built on their deployment, namely, the centralised IDS (CIDS), distributed IDS (DIDS), and hybrid IDS (HIDS). In CIDS, the analysis is carried out only on centralized servers, where they control all the devices present in the network. In this scheme, the IDS is normally placed on a centralized point of control for the devices [79] like end servers, cluster heads, and routers [83]. The IDS analyses the data available in the network traffic to detect intrusions [84]. The next type of IDSs used in IoT security is the distributed IDSs. In this scheme, the IDSs are deployed on the sensing nodes in the IoT devices. Each of the sensors will be able to analyse the sensed data to identify the behaviour of the nodes in IoT devices in order to detect the intrusions. The hybrid IDS is a collective mixture of centralized and distributive IDSs. The concept of this scheme is that the IDS is placed both in centralized servers and also in the sensing devices present in the IoT environment. The advantage of hybrid IDS is that intrusions can be detected both in the centralized server and also in the sensing devices [80].


## 4. Classification of IDS in the IoT

Figure1 shows the taxonomy of IDS in IoT. The IDSs in the IoT are categorized into three groups, namely, IDSs based on the intrusion detection mechanism used [14, 85–88], IDSs where the detection is based on network structure, and IDSs developed by focusing on attack types.Figure 1
Taxonomy of IDS in IoT.The IDS-based mechanism is further subdivided into four categories, namely, anomaly detection, signature detection, and specification and hybrid IDS. The IDS detection based on network structure is further classified into CIDS, DIDS, and HIDS. The intrusion detection based on attacks is further classified into IDS for detecting denial of service attacks, reply attacks, Sybil attacks, wormhole attacks, false data injection attacks, and jamming attacks.


### 4.1. IDS Based on Anomaly Detection

Anomaly intrusion detection is a technique [89, 90] used to differentiate the normal behaviour of the devices from the abnormal behaviour. To detect the intrusion based on an anomaly, the behaviour of the devices is compared with the normal behaviour and a threshold (TH) value is used to find out if there is any deviation by a device that exceeds the threshold. Such a device will be labelled as a suspected device and will be observed over a period of time. If the anomaly in the behaviour continues in a device, it will be treated as a malicious device and will be isolated from communication with other devices. Various authors have proposed different techniques for providing security to the IoT environment based on anomaly detection.Fu et al. [91] proposed an IDS technique on detecting various attacks that was developed using the mining technique. They have employed intrusion semantic techniques to detect the misbehaviour of the devices in an IoT environment. Their proposed system uses slice time window technique for accomplishing the intrusion detection. In this technique, the collected information about the devices is classified based on time analysis. In this system, the anomaly is detected by equating the existing data with the normal profile and checking whether there is a deviation. If the data are inconsistent, then it is considered as intrusion. This technique has been evaluated based on theoretical analysis. However, there exists significant complexity in the comparison of data in real time, and hence, the network life is affected considerably. Ding et al. [92] suggested an innovative theory-based technique to detect the anomalies of devices in an IoT environment. In this system, information security is provided to the devices which utilize most network resources. The proposed system monitors malicious devices in order to identify selfish devices and intruders. Moreover, in this game-based model, each of the devices is allowed to use an optimal quantity. The devices utilize the network resources during data transmission to monitor and ensure against malicious devices which can cause vulnerability to the network. The advantage of this system includes its ability to detect the normal behaviour in the system. The constraint of this model is the lack of required detection accuracy.Ragasegarar et al. [93] proposed distributed anomaly detection architecture for providing security to the devices. In this architecture, the grouping of devices is made using a hyperellipsoidal plane method. The information available on each device is used to detect the neighbourhood behaviour with respect to the abnormality of the devices both locally and globally that are present in the group. The devices collect the information available to identify the local and global abnormalities in the behaviour of the devices. Moreover, when the devices sense the data from the sensing domain, anomalies are detected based on the collected data. The advantages are the upsurge in network life time and reduction in computation overhead. The main limitation is the reduction in intrusion detection accuracy when the width of the hyperellipsoidal plane is expanded. Chen et al. [94] proposed a fusion-based protective technique to provide better defence against the attacks caused by the intruders. In this method, each device sends a one-bit message to the fusion cache for intrusion detection. The benefit of this method is that it is robust by nature. Moreover, the proposed method is not accurate in detecting unknown types of attacks.Ham et al. [95] suggested an efficient anomaly-based intrusion detection technique to detect malware in the android operating system using a linear support vector machine. The benefits of this method are that it affords true positive and intrusion detection accuracy when it is equated with other existing approaches. The limitations are the existence of significant overhead in terms of computation. Moreover, the proposed method requires heavy implementation which can exhaust both the network and system resources of the devices in an IoT environment. Wang et al. [96] proposed an efficient security mechanism which can train and detect the intrusions in IoT devices in large scale to provide efficient IoT security services. The advantages of this method are in its ability to provide efficient intrusion detection at real time with better accuracy. Moreover, this method optimizes both system performance and network life time. The limitations are that it consumes more energy and will exhaust both network and system resources. Moreover, it is able to identify only a limited number of known attacks in the IoT environment.Pongle and Chavan [33] proposed an IDS which is able to identify the worm hole assault based on their neighbour device and location information. The advantages of this method are its energy efficiency and real-time intrusion detection capability. Moreover, the proposed intrusion detection system improves QoS by decreasing packet overhead and improving the packet delivery ratio. Moreover, the proposed method is able to detect only a single type of attack which can be either known attacks or unknown attacks. Cervantes et al. [97] proposed an efficient IDS which is able to identify sinkhole attacks using watchdog and trust [98] management mechanisms to monitor the behaviour of the devices in an IoT environment. The advantages of this method are that it optimizes both network and system energy efficiency and improves QoS in the network in terms of minimizing packet and routing overheads, and it increases the packet delivery ratio. Moreover, the proposed method has low false-positive and false-negative rates. However, this model is able to detect only a limited number of attacks. Moreover, the proposed IDS is complex in terms of high computation overhead which is not desired in the IoT.Summerville et al. [99] suggested a lightweight intrusion detection outline which is able to detect the various attacks. The proposed system detects the intrusions using deep packet analysis and the intrusion detection scheme is deployed in the IoT devices. The advantage of this method lies in its accurate intrusion detection capability. Moreover, the proposed IDS is lightweight in nature and has a low false positive intrusion detection rate. It enhances the QoS by reducing the communication overhead in the network. On the other hand, there exist significant computation and communication overheads during packet classification which consumes more energy and time. Moreover, the proposed IDS can only detect the known types of assaults which can be detected from the routed packets sent from the other devices in the IoT environment. Eliseev and Gurina [100] proposed an intrusion scheme which is able to observe the abnormal behaviour of the devices in the IoT environment. Their proposed intrusion system uses a correlation function which is based on the request–response method. The benefit of the planned method is that it is lightweight in nature and thereby consumes only the optimal quantity of resources in the network. Moreover, their suggested intrusion detection scheme offers an improved accuracy rate. The limitations are its queuing delay and communication delay. Furthermore, the suggested IDS is complex in terms of computational overhead which may exhaust the network resources quickly.Grgic et al. [101] suggested a security framework for devices in the IoT which can identify the malevolent nodes in IPV6-based distributed systems. The proposed framework monitors the anomalous behaviour of the devices using collaborative processing to identify the attack. The advantages of the system framework are its energy efficiency and better intrusion detection accuracy. The limitations are its high false positive rate and that it will be able to detect only known types of assaults in an IoT environment. Sonar and Upadhyay [102] designed a system which can identify various attacks using intellectual agents. In this model, the intelligent agents [103] are placed in the network server, and gateway devices are used to monitor the behaviour of incoming data traffic. The proposed system employs a blacklist and greylist colour differentiator to show the difference between malicious devices and legitimate devices. The advantages of their work are its low false high true positive rate. Moreover, the proposed work has better intrusion detection accuracy. The limitations are that the proposed IDS will not be viable for implementation in a reasonable amount of time and it does not provide device scalability.Hodo et al. [104] suggested a system which can detect distributed DoS attacks using a three-layer artificial neural network (ANN). Four nodes act as the client, and one node acts as the server to perform data analytics. The server acts as a sink which receives the requests from the clients and responds to their requests. The advantages are that an expert system is built using a knowledge-based approach to efficiently analyse and detect DDoS attacks from the data packets which have been received from the server. Moreover, the proposed model optimizes the resources in real time and has a better ability to detect the malicious activities of the nodes. The limitations are that intrusion detection accuracy depends on probability estimation. Moreover, the expert system that uses the knowledge base should be trained more accurately to get better results. Table 1 gives the comparison of different IDSs based on anomaly detection.Table 1
Comparison of different intrusion detection systems based on anomaly detection.
Author nameMechanism usedAdvantagesLimitationsFu et al. [91]Anomalies detection based on hierarchical distributed scheme(1) Adaptive in nature(1) Not suitable to handle unknown type of attacks(2) Low false positive rate(2) High latency(3) Less resource consumptionDing et al. [92]Providing security with non-cooperative based game theory(1) High intrusion detection accuracy(1) Low combination for IoT devices(2) Low computation overheadRajasegarar et al. [93]Hyperellipsoidal cluster-based anomalies detection(1) Better intrusion detection accuracy(1) Intrusion accuracy deviates when width of hyperellipsoidal plane expands(2) Low communication overhead(3) Better utilization of network resourcesChen et al. [94]Fusion-based intrusion defence mechanism to limit the attack damage(1) Better robust in nature(1) Network tropology known to attackers(2) Low communication overhead(2) Detects only limited number of known attacks(3) Complex implementationHam et al. [95]Direct SVM built android malware detection(1) Better true positive rate and low false alarm(1) More overhead(2) Better intrusion detection accuracy(2) Complex implementation(3) Detects only limited number of assaultsWang et al. [96]Detecting intrusion based on online for large scale IoT devices(1) Intrusion detection based on real time(1) Only detects the limited set of attacks(2) Better optimization of network resources(2) Complex implementation(3) Better computation overheadPongle and Chavan [33]Wormhole detection attack based on node location and information from neighbour nodes(1) Better energy efficiency(1) High false positive rate(2) Low overhead(2) Only known type of attacks are detected(3) Better intrusion detection accuracyCervantes et al. [97]INTI based sink hole attack detection(1) Better resource utilization(1) Can only detects only known types of attacks(2) Low false positive rate and negative rate(2) High overheadEliseev and Gurina [100]Correlation function-based anomalies detection behaviour of the network server using the request-response method(1) Lightweight IDS implementation(1) Consumes more network resources(2) Better reliability(2) High computation and computation overhead(3) High intrusion detection accuracy(3) Fails to detect the unknown types of attacksGrgic et al. [101]Malicious nodes detection in IPV6 IoT environment using adaptive distributed systems(1) Better energy efficiency(1) Detects only some limited number of known attacks(2) Better intrusion detection accuracy(2) Has high false positive rate(3) Tolerant to device failureSonar and Upadhyay [102]Agent-based DDoS attack detection using the black list and grey list method(1) Has low false positive and better true positive rate(1) Cannot be implemented in real time(2) Not scalable in nature(2) Better intrusion detection accuracy(3) Only detects small number of known attacksHodo et al. [104]ANN based detection of DoS attacks in IoT environment using MLP supervised learning(1) Knowledge-based expert system(1) Probability-based estimation intrusion detection(2) Better network resource optimization(3) Better intrusion detection accuracy even with incomplete data(2) More training is needed to get accurate detection of attacks


### 4.2. IDS Based on Signature

An IDS based on signature provides better defence against the various network attacks based on the generated signature. In this system, the current behaviour of the network is matched with the malicious attack patterns to trace the type of attacks generated by the intruder. Many authors have proposed methods for providing security to IoT environments using a signature-based intrusion detection approach.Amin et al. [105] suggested a signature for securing the Internet-protocol-based ubiquitous sensor networks. In this IDS, the signature for various attack patterns are generated. The generated attack patterns are stored as an array and kept in the bloom filter. When the data packets enter the bloom filter, it matches the attack pattern and filters them. Based on the pattern match, the type of attack is detected. The advantages of this method are that it has a lower false alarm rate and provides better intrusion detection accuracy. Moreover, the proposed model is lightweight in nature, and hence, it can perform better optimization [106] of the network resources. The limitations are that it can only detect a fixed pattern of attacks. Moreover, there is communication overhead during data transmission from nodes to the bloom filter. Oh et al. [107] designed an IDS, which can identify the various attack patterns using a matching engine. Here, the matching engine uses auxiliary shifting for the early identification of attacks. By doing so, the attacks can be early detected based on the matching pattern and can terminate the attack as early as possible. The advantages of the system are that it has improved computational complexity and has high intrusion detection accuracy with fixed, generated attack signature patterns. Moreover, it is scalable and ensures better memory utilization. The limitations are that it can only detect a finite number of known attacks based on the pregenerated signature patterns. Moreover, the proposed system cannot be implemented in real time.Sun et al. [108] proposed an intrusion detection scheme which is able to detect malicious assaults using cloud eye in an IoT-based cloud environment. On the device side, the cloud eye uses an intelligent lightweight agent scanner to detect the malicious data from the incoming data packets. The server side of the cloud eye consists of a large database which can store the predefined attack patterns and is updated periodically. Their system employs Suspicious Bucket Cross Filtering (SBCF) to detect the malicious data from the data packets. The type of attack is identified based on matching patterns with a predefined attack signature pattern. The advantages of this system are that it provides trusted and secured services [109] without compromising privacy. Moreover, the proposed intrusion detection system provides better resource optimization and can efficiently detect attacks with limited predefined pattern signatures. The limitations are that it consumes more memory and has the ability to detect only a limited number of known and familiar attacks based on the pregenerated signature patterns. Table 2 shows the comparison of various IDSs developed based on the signature patterns.Table 2
Comparison of IDS based on signature.
AuthorsMethodologiesAdvantagesLimitationsAmin et al. [105]Network intrusion-based detection system for IP-USN(1) Better false alarm rate(1) Moreover overhead due to redundant data transmission(2) Better intrusion detection accuracy(2) Detects only limited number of assaults(3) Better optimization of network resources(4) Lightweight in natureOh et al. [107]Pattern matching engine based malicious node detection(1) Low computational overhead(1) Detects only limited set of fixed attacks(2) High intrusion detection accuracy with limited pregenerated signatures(2) Real time implementation is not possible(3) Scalable in natureSun et al. [108]Malicious bodes detection system based on cloud eye an antimalware detection system(1) Secured and trusted service with privacy(1) Consumes more memory(2) Better time and space complexity(2) Detects only very few type of attacks(3) Better intrusion detection accuracy


### 4.3. IDS Based on Specification

IDS based on specification detects the intrusion specification normally. The detected intrusions are captured by the legitimate system for further analysis. In the past, many researchers have designed many IDSs based on their specifications to provide enhanced security to the IoT environment. Some of IDSs based on its specification are discussed in this section. Misra et al. [110] planned an IDS with the service oriented architecture (SOA) model in the IoT environment. In this IDS, the SOA is configured to act as middleware to provide the services to the IoT applications. The proposed IDS sends a DALERT control message to all the available nodes. When the requests by the particular devices to middleware exceed the limit which is set as threshold value, the system detects the possibility of vulnerability in the network. Based on this information, the network administrator detects the intrusions. The limitations are it has high false positive rate and it cannot be implemented in the real time. Moreover, this IDS can be able to detect only the intrusions and it is not able to confirm the attack. Murynets and Jover [111] designed an intrusion detection system which is able to detect intrusions from short message sender (SMS) by using volumetric and content-based techniques. The aim of volumetric analysis is to detect the intrusion based on the deviation of the predefined pattern. The main aim of the content-based algorithm is to track the devices in the IoT environment. By muting these two algorithms, the independent DoS attack can be detected efficiently. The advantages of these IDSs are that they provide better intrusion detection accuracy. The limitations are that it has complex implementation tasks and does not have the ability to detect intrusions in real time.Xia et al. [112] designed a new IDS which can identify the node internal attacks in the IoT environment. This IDS has been designed along with a privacy aware routing protocol which ensures the privacy of the nodes in the network. In the route maintenance phase, the malicious activities are detected with the help of neighbour nodes and also based on the traffic analysis of the node’s past behaviour. The advantages are that it has better intrusion detection accuracy and a low expectancy. The limitations are its communication and computation overheads. Moreover, the system can identify only a restricted number of assaults. La et al. [113] proposed an IDS based on innovative model which can detect deceptive assaults. The system employs honeypots as defence tool, which is capable of analysing the incoming data packets based on the predefined intelligent rules. Any suspected data packets are further analysed by the honeypots. The advantage of this IDS is its better intrusion detection accuracy. The limitations are its overhead in the intrusion detection process which consumes more network resources in the IoT environment.Ahmed and Ko [36] designed an IDS which can provide defence against the black hole attack. In local decision procedure, the data about relationships among the nodes is gathered by the neighbours to detect the malicious behaviours. The validity of the malevolent nodes identified at the local decision process is further verified by the global node verification phase. The advantages of this IDS are its real-time intrusion detection and its intrusion detection exactness. Furthermore, the recommended IDS has a better packet delivery ratio and provides good defence against black hole attacks in an IoT environment. The limitations are that it is planned only to detect black hole attacks and has high false positive and low true positive rates. Moreover, the accuracy of IDS decreases as the count of infected nodes increases.Surendar and Umamakeswari [114] planned an IDS which can identify sinkhole assaults using intrusion detection based on a constraint-based specification model with a request-response method in the IoT environment. The observer node plays a vital role by checking the behaviour of all nodes to identify the nodes which drop packets. The advantages of this IDS are its low storage and computational overhead. The limitations are that intrusions are not detected in real time. Moreover, intrusion accuracy is inversely proportional to the total number of infected nodes. Fu et al. [115] proposed an IDS which can detect attacks using an automata model in the IoT environment. The proposed IDS has four main mechanisms, namely, event monitor, event data base, event analyser, and response event. The vital role of the event monitor is to check the activities in the network and transmit them in digital format to the event analyser. The role of the event database is to store the recorded events and differentiate them into normal data and abnormal data. The role of the event analyser is to analyse the stored data, and it works based on three submodules, namely, network structure learning module, action flow abstraction learning module, and intrusion detection module. The benefits of this IDS are its easy implementation and better intrusion detection rate. The limitations are its communications overhead and increased resource consumption. Moreover, this IDS has more delay and can be applied only to delay-tolerant networks.Bose et al. [116] suggested an IDS which is able to detect the selective forwarding attack in the IoT environment. This IDS detects the intrusion in two ways, namely, CIDS and DIDS. In CIDS, the IDS is placed in the sink to detect the malicious nodes. In a distributed IDS, the intrusion detection scheme is located in the routing nodes. In this IDS, the nodes are monitored at two places. The first level of monitoring is carried out by the router nodes, and the second level of monitoring is carried out by the sink. Initially, the router nodes checked the performance of their neighbour and sent it to the sink. The sink cross checks the behaviour of the nodes and identifies the nodes that drop packets frequently. The nodes which release packets frequently are called “malevolent nodes,” and they are isolated from the network. The advantages of this IDS are its easy implementation with less complexity. The limitations are its low intrusion detection accuracy and the fact that it consumes more network resources. Moreover, the proposed IDS has high computation and communication overhead.Liu et al. [117] have designed an IDS which can identify the intrusion. The proposed IDS classifies the data into low-risk and high-risk data. Moreover, SFC and PCA algorithms are employed for self-adjustment in the detection frequency. The advantages of this intrusion detection system are its high adaptiveness and better handling of false data alarm. The limitations are its false positive rate. Moreover, the intrusion detection accuracy decreases when the volume of the data increases by the devices in IoT environment. Table 3 gives the comparison of IDSs developed based on specification.Table 3
Comparison of IDS based on specification.
AuthorsMethodologiesAdvantagesLimitationsMisra et al. [110]SOA-based attack detection based on the system model in IoT environment(1) Better optimization of network resources(1) High true positive and false negative rates(2) Better intrusion detection accurateness(2) Inability to detect the intrusion at the real time(3) Implementation is very complexMurynets and Jover [111]Contact- and volumetric-based intrusion detection for SMS in IoT environment(1) Better intrusion detection rate(1) Implementation is complex(2) Better resource optimization(2) Not a real-time IDSXia et al. [112]An incentive-based internal attack detection mechanism based on neighbour nodes to provide truthful information(1) Better intrusion detection rate(1) Overhead in terms of communication and computation(2) Low delay(2) Can only detect the known number of limited attacks(3) Provides trust-based security during intrusion detectionLa et al. [113]Game theory model-based deceptive attack detection for honeypot-based IoT network(1) Better intrusion detection accuracy(1) High overhead in terms of network resources(2) Real-time-based intrusion detection(2) It has more coverage timeAhmed and Ko [36]Detection of black hole attack using efficient mitigations techniques for RPL networks in IoT environment(1) Real-time intrusion detection with better accuracy(1) This IDS detects only black hole attack(2) Enhancement in packet delivery ratio (PDR)(2) The intrusion accuracy decreases when the number of infected nodes increases(3) Better false positive rate(3) Overhead in terms of communicationSurendar and Umamakeswari et al. [114]Specification based sink hole attack detection in IoT networks(1) Better overhead in terms of storage(1) Intrusion accuracy is indirectly proportional with number of infected nodes(2) Better optimization of network resourcesFu et al. [115]Automata model-based uniform intrusion detection in IoT networks(1) Its easy implementation and real time detection of intrusion(1) High consumption of network resources(2) Less complexity(2) High latency and computational overhead(3) Better intrusion detection accuracy(3) It has high false positive rate when the percentage of infected node increasesBose et al. [116]Sequential probability ratio test-based selective forward attack detection based on probability of packet drop in IoT environment(1) Better true positive rate with easy implementation(1) High consumption of network resources(2) Complexity is low(2) Overhead in terms of communication and has high delay(3) False positive rate is high when the percentage of infected nodes increasesLiu et al. [117]SFC and PCA algorithms-based attack detection in IoT environment(1) Adaptiveness(1) The intrusion detection decreases when the volume of the data increases(2) Better false data alarm(2) Resource consumption is high(3) Better intrusion detection accuracy


### 4.4. IDS Based on the Hybrid Detection Method

The IDSs based on the hybrid method detect the abnormal behaviour of the nodes by combining the IDS based on anomaly detection and signature generation. The capability of the IDS is that it can identify the unknown types of attacks more efficiently which were not detected in IDS-based anomaly detection and signature generation.Various authors have proposed many IDSs based on the hybrid approach to enhance the security of devices in the IoT environment. Amin et al. [118] have designed the security framework which is able to detect attacks in the Internet protocol–ubiquitous sensor network (IP-USN). The proposed framework works on two modules, namely, Internet packet analyser (IPA) and the USN packet analyser. The main aim of these modules is to provide efficient analysis of the incoming data traffic. IPA module is further subdivided into two modules, namely, anomaly detector and pattern classifier. The vital role of the attack detector is to detect the various behaviours of the devices. The packet analyser detects the abnormal behaviour of the devices and maps it to the predefined patterns using a machine learning algorithm and groups them under a common label. The advantages of this IDS are its lightweight property and it has better false error detection rate. Moreover, the proposed IDS has a high positive intrusion detection rate. The limitations are the overhead of computation and the vulnerability to delay during intrusion detection.Kasinathan et al. [119] have designed a security framework which can detect DoS attacks on the 6LoWPAN (IPv6 over Low-Power Private Area Network) in the IoT environment. The proposed framework efficiently analysed the incoming data packets in 6LoWPAN to detect the anomalies in the network. Once the intrusion is detected, the alarm is raised to the decision manager. The DoS protection manager validates the intrusion with preloaded signatures which are stored in the database. If the detected anomaly matches with the predefined digital signatures. The advantage of this IDS is false intrusion detection rate that is decreased and improved availability. The limitations are that this IDS can be implemented only to the network which is operated only in dynamic topology. Moreover, the predefined signatures are not updated frequently by the DoS protection manager.Raza et al. [120] suggested an IDS which can identify the assaults based on routing in the IoT environment. The suggested IDS is designed using three modules. The first module consists of 6LoWPAN mapper; the vital role of the module is to gather the information in the network. The role of the second module is to analyse the collected information and detect intrusions in the network. The third module comprises of distributed firewall; the abnormal activities enter into the network and also handle packet drops in the network. The advantages of it are that this IDS can be extended to other networks. Moreover, the proposed IDS can be implemented in real time and has a high positive intrusion detection rate. The limitations are that it consumes more network resources and has improved communication and computation overhead.Matsunaga and Toyoda [121] designed an IDS which can be able to detect the intrusion based on neighbour node broadcasting the latest rank based on the time stamp method. The system functions in two steps. In the first step, each node broadcasts its ranks to its neighbour nodes. In the second stage, the time stamp has been attached to each node to validate any anomalies. The advantage of this IDS is that it has more true positive intrusion detection rate, and it is highly scalable in nature. The limitations are it has overhead in terms of computation and communication. Moreover, the proposed IDS consumes a lot of resources which affects the network’s consistency.Sedjelmaci et al. [122] suggested an IDS based on a game theory approach with a hybrid intrusion detection method. The proposed IDS uses both anomaly- and signature-based methods to detect intrusions in the network. Nash equilibrium (NE) is computed for all the nodes. The computed NE score and the type of intrusion detection method employed are decoded. The advantage of this IDS is that it has low overhead. Moreover, the system has an improved intrusion detection rate and is lightweight in nature. The limitations are that it has a high latency and complex computational overhead. Shreenivas et al. [123] suggested an IDS where the attacks are detected based on EthereumX (ETX) value and geographical hints. The proposed IDS uses 6mapper to monitor the nodes behaviour in a directed acyclic graph (DAG) based on calculated ETX values, and the intrusion is detected. The advantages of this IDS are its improved intrusion detection accuracy and its real-time implementation. The limitations are its increased false positive intrusion detection accuracy.Midi et al. [124] suggested an IDS which can detect attacks based on expert knowledge driven in the IoT environment. The expert knowledge driven system monitors the network to sense intrusions in the network. The advantages of this IDS are its high intrusion detection accuracy and better optimization of the network resources in terms of RAM and CPU usage. The limitations are its computation overhead which affects the network performance considerably. Sedjelmaci and Senouci [125] suggested an IDS which can detect wormhole and sinkhole attacks based on the game theory model in the IoT environment. The advantages of this IDS are its high intrusion detection accuracy and its lightweight nature. Moreover, the proposed IDS optimizes network resources. The limitations are that it has computational overhead which can affect the network performance considerably. Table 4 gives the comparison of hybrid-based intrusion detection methods.Table 4
Comparison of hybrid-based intrusion detection methods used in the IoT environment.
AuthorsMethodologiesAdvantagesLimitationsAmin et al. [118]Detection of attacks using generalised architecture for IP-USN in IoT environment(1) Better false intrusion detection rate(1) Overhead in terms of computation(2) Better optimization of network resources(2) High latencyKasinathan et al. [119]Detection of abnormal behaviour of nodes and their matching signature using DoS protection manger(1) IDS based on real time(1) Works only with the network with dynamic tropology(2) Better false intrusion detection alarm(2) The pregenerated signatures are not frequently updated by DoS protection manager(3) Better resource optimization1Raza et al. [120]Detection of routing attacks based on integrated mini firewall-based anomaly detection and distributed firewall-based signature generation IDS in IoT environment(1) Intrusion detection in real time(1) Resource consumption by the network is high(2) Overhead is minimal(2) False intrusion detection rate is high(3) Better intrusion detection accuracyMatsunaga and Toyoda [121]Detection of attacks based timestamp to detect the inconsistency of nodes during broadcast of rank to the neighbour nodes(1) Better overhead(1) High consumption of network resources(2) False data intrusion detection is low(2) High overhead in terms of computation(3) Better intrusion detection accuracySedjelmaci et al. [122]Game theory-based effective attack detection using anomaly and signature detection techniques for IoT networks(1) Better intrusion detection accuracy(1) High latency(2) Computational overhead is high(2) Better false intrusion detection rate(3) Network resources are not optimized(3) Energy consumption by the nodes are highShreenivas et al. [123]ETX metric and geographical hints-based attack detection in IoT(1) Intrusion detection in real time(1) High false positive intrusion detection rate(2) Better power optimization(2) Overhead in terms of computation and communicationMidi et al. [124]Knowledge driven expect real time-based IDS for self-adapting IoT networks(1) Better intrusion detection accuracy(1) Overhead in terms computation and communication(2) Better utilization network and system resources(3) Low false intrusion detection rateSedjelmaci and Senouci [125]Game theory-based worm hole, sink hole and black hole attack detection in IoT environment(1) Better intrusion detection accuracy(1) Overhead in terms computation and communication(2) Delay is minimal(3) Network resource consumption is low


## 4.1. IDS Based on Anomaly Detection

Anomaly intrusion detection is a technique [89, 90] used to differentiate the normal behaviour of the devices from the abnormal behaviour. To detect the intrusion based on an anomaly, the behaviour of the devices is compared with the normal behaviour and a threshold (TH) value is used to find out if there is any deviation by a device that exceeds the threshold. Such a device will be labelled as a suspected device and will be observed over a period of time. If the anomaly in the behaviour continues in a device, it will be treated as a malicious device and will be isolated from communication with other devices. Various authors have proposed different techniques for providing security to the IoT environment based on anomaly detection.Fu et al. [91] proposed an IDS technique on detecting various attacks that was developed using the mining technique. They have employed intrusion semantic techniques to detect the misbehaviour of the devices in an IoT environment. Their proposed system uses slice time window technique for accomplishing the intrusion detection. In this technique, the collected information about the devices is classified based on time analysis. In this system, the anomaly is detected by equating the existing data with the normal profile and checking whether there is a deviation. If the data are inconsistent, then it is considered as intrusion. This technique has been evaluated based on theoretical analysis. However, there exists significant complexity in the comparison of data in real time, and hence, the network life is affected considerably. Ding et al. [92] suggested an innovative theory-based technique to detect the anomalies of devices in an IoT environment. In this system, information security is provided to the devices which utilize most network resources. The proposed system monitors malicious devices in order to identify selfish devices and intruders. Moreover, in this game-based model, each of the devices is allowed to use an optimal quantity. The devices utilize the network resources during data transmission to monitor and ensure against malicious devices which can cause vulnerability to the network. The advantage of this system includes its ability to detect the normal behaviour in the system. The constraint of this model is the lack of required detection accuracy.Ragasegarar et al. [93] proposed distributed anomaly detection architecture for providing security to the devices. In this architecture, the grouping of devices is made using a hyperellipsoidal plane method. The information available on each device is used to detect the neighbourhood behaviour with respect to the abnormality of the devices both locally and globally that are present in the group. The devices collect the information available to identify the local and global abnormalities in the behaviour of the devices. Moreover, when the devices sense the data from the sensing domain, anomalies are detected based on the collected data. The advantages are the upsurge in network life time and reduction in computation overhead. The main limitation is the reduction in intrusion detection accuracy when the width of the hyperellipsoidal plane is expanded. Chen et al. [94] proposed a fusion-based protective technique to provide better defence against the attacks caused by the intruders. In this method, each device sends a one-bit message to the fusion cache for intrusion detection. The benefit of this method is that it is robust by nature. Moreover, the proposed method is not accurate in detecting unknown types of attacks.Ham et al. [95] suggested an efficient anomaly-based intrusion detection technique to detect malware in the android operating system using a linear support vector machine. The benefits of this method are that it affords true positive and intrusion detection accuracy when it is equated with other existing approaches. The limitations are the existence of significant overhead in terms of computation. Moreover, the proposed method requires heavy implementation which can exhaust both the network and system resources of the devices in an IoT environment. Wang et al. [96] proposed an efficient security mechanism which can train and detect the intrusions in IoT devices in large scale to provide efficient IoT security services. The advantages of this method are in its ability to provide efficient intrusion detection at real time with better accuracy. Moreover, this method optimizes both system performance and network life time. The limitations are that it consumes more energy and will exhaust both network and system resources. Moreover, it is able to identify only a limited number of known attacks in the IoT environment.Pongle and Chavan [33] proposed an IDS which is able to identify the worm hole assault based on their neighbour device and location information. The advantages of this method are its energy efficiency and real-time intrusion detection capability. Moreover, the proposed intrusion detection system improves QoS by decreasing packet overhead and improving the packet delivery ratio. Moreover, the proposed method is able to detect only a single type of attack which can be either known attacks or unknown attacks. Cervantes et al. [97] proposed an efficient IDS which is able to identify sinkhole attacks using watchdog and trust [98] management mechanisms to monitor the behaviour of the devices in an IoT environment. The advantages of this method are that it optimizes both network and system energy efficiency and improves QoS in the network in terms of minimizing packet and routing overheads, and it increases the packet delivery ratio. Moreover, the proposed method has low false-positive and false-negative rates. However, this model is able to detect only a limited number of attacks. Moreover, the proposed IDS is complex in terms of high computation overhead which is not desired in the IoT.Summerville et al. [99] suggested a lightweight intrusion detection outline which is able to detect the various attacks. The proposed system detects the intrusions using deep packet analysis and the intrusion detection scheme is deployed in the IoT devices. The advantage of this method lies in its accurate intrusion detection capability. Moreover, the proposed IDS is lightweight in nature and has a low false positive intrusion detection rate. It enhances the QoS by reducing the communication overhead in the network. On the other hand, there exist significant computation and communication overheads during packet classification which consumes more energy and time. Moreover, the proposed IDS can only detect the known types of assaults which can be detected from the routed packets sent from the other devices in the IoT environment. Eliseev and Gurina [100] proposed an intrusion scheme which is able to observe the abnormal behaviour of the devices in the IoT environment. Their proposed intrusion system uses a correlation function which is based on the request–response method. The benefit of the planned method is that it is lightweight in nature and thereby consumes only the optimal quantity of resources in the network. Moreover, their suggested intrusion detection scheme offers an improved accuracy rate. The limitations are its queuing delay and communication delay. Furthermore, the suggested IDS is complex in terms of computational overhead which may exhaust the network resources quickly.Grgic et al. [101] suggested a security framework for devices in the IoT which can identify the malevolent nodes in IPV6-based distributed systems. The proposed framework monitors the anomalous behaviour of the devices using collaborative processing to identify the attack. The advantages of the system framework are its energy efficiency and better intrusion detection accuracy. The limitations are its high false positive rate and that it will be able to detect only known types of assaults in an IoT environment. Sonar and Upadhyay [102] designed a system which can identify various attacks using intellectual agents. In this model, the intelligent agents [103] are placed in the network server, and gateway devices are used to monitor the behaviour of incoming data traffic. The proposed system employs a blacklist and greylist colour differentiator to show the difference between malicious devices and legitimate devices. The advantages of their work are its low false high true positive rate. Moreover, the proposed work has better intrusion detection accuracy. The limitations are that the proposed IDS will not be viable for implementation in a reasonable amount of time and it does not provide device scalability.Hodo et al. [104] suggested a system which can detect distributed DoS attacks using a three-layer artificial neural network (ANN). Four nodes act as the client, and one node acts as the server to perform data analytics. The server acts as a sink which receives the requests from the clients and responds to their requests. The advantages are that an expert system is built using a knowledge-based approach to efficiently analyse and detect DDoS attacks from the data packets which have been received from the server. Moreover, the proposed model optimizes the resources in real time and has a better ability to detect the malicious activities of the nodes. The limitations are that intrusion detection accuracy depends on probability estimation. Moreover, the expert system that uses the knowledge base should be trained more accurately to get better results. Table 1 gives the comparison of different IDSs based on anomaly detection.Table 1
Comparison of different intrusion detection systems based on anomaly detection.
Author nameMechanism usedAdvantagesLimitationsFu et al. [91]Anomalies detection based on hierarchical distributed scheme(1) Adaptive in nature(1) Not suitable to handle unknown type of attacks(2) Low false positive rate(2) High latency(3) Less resource consumptionDing et al. [92]Providing security with non-cooperative based game theory(1) High intrusion detection accuracy(1) Low combination for IoT devices(2) Low computation overheadRajasegarar et al. [93]Hyperellipsoidal cluster-based anomalies detection(1) Better intrusion detection accuracy(1) Intrusion accuracy deviates when width of hyperellipsoidal plane expands(2) Low communication overhead(3) Better utilization of network resourcesChen et al. [94]Fusion-based intrusion defence mechanism to limit the attack damage(1) Better robust in nature(1) Network tropology known to attackers(2) Low communication overhead(2) Detects only limited number of known attacks(3) Complex implementationHam et al. [95]Direct SVM built android malware detection(1) Better true positive rate and low false alarm(1) More overhead(2) Better intrusion detection accuracy(2) Complex implementation(3) Detects only limited number of assaultsWang et al. [96]Detecting intrusion based on online for large scale IoT devices(1) Intrusion detection based on real time(1) Only detects the limited set of attacks(2) Better optimization of network resources(2) Complex implementation(3) Better computation overheadPongle and Chavan [33]Wormhole detection attack based on node location and information from neighbour nodes(1) Better energy efficiency(1) High false positive rate(2) Low overhead(2) Only known type of attacks are detected(3) Better intrusion detection accuracyCervantes et al. [97]INTI based sink hole attack detection(1) Better resource utilization(1) Can only detects only known types of attacks(2) Low false positive rate and negative rate(2) High overheadEliseev and Gurina [100]Correlation function-based anomalies detection behaviour of the network server using the request-response method(1) Lightweight IDS implementation(1) Consumes more network resources(2) Better reliability(2) High computation and computation overhead(3) High intrusion detection accuracy(3) Fails to detect the unknown types of attacksGrgic et al. [101]Malicious nodes detection in IPV6 IoT environment using adaptive distributed systems(1) Better energy efficiency(1) Detects only some limited number of known attacks(2) Better intrusion detection accuracy(2) Has high false positive rate(3) Tolerant to device failureSonar and Upadhyay [102]Agent-based DDoS attack detection using the black list and grey list method(1) Has low false positive and better true positive rate(1) Cannot be implemented in real time(2) Not scalable in nature(2) Better intrusion detection accuracy(3) Only detects small number of known attacksHodo et al. [104]ANN based detection of DoS attacks in IoT environment using MLP supervised learning(1) Knowledge-based expert system(1) Probability-based estimation intrusion detection(2) Better network resource optimization(3) Better intrusion detection accuracy even with incomplete data(2) More training is needed to get accurate detection of attacks


## 4.2. IDS Based on Signature

An IDS based on signature provides better defence against the various network attacks based on the generated signature. In this system, the current behaviour of the network is matched with the malicious attack patterns to trace the type of attacks generated by the intruder. Many authors have proposed methods for providing security to IoT environments using a signature-based intrusion detection approach.Amin et al. [105] suggested a signature for securing the Internet-protocol-based ubiquitous sensor networks. In this IDS, the signature for various attack patterns are generated. The generated attack patterns are stored as an array and kept in the bloom filter. When the data packets enter the bloom filter, it matches the attack pattern and filters them. Based on the pattern match, the type of attack is detected. The advantages of this method are that it has a lower false alarm rate and provides better intrusion detection accuracy. Moreover, the proposed model is lightweight in nature, and hence, it can perform better optimization [106] of the network resources. The limitations are that it can only detect a fixed pattern of attacks. Moreover, there is communication overhead during data transmission from nodes to the bloom filter. Oh et al. [107] designed an IDS, which can identify the various attack patterns using a matching engine. Here, the matching engine uses auxiliary shifting for the early identification of attacks. By doing so, the attacks can be early detected based on the matching pattern and can terminate the attack as early as possible. The advantages of the system are that it has improved computational complexity and has high intrusion detection accuracy with fixed, generated attack signature patterns. Moreover, it is scalable and ensures better memory utilization. The limitations are that it can only detect a finite number of known attacks based on the pregenerated signature patterns. Moreover, the proposed system cannot be implemented in real time.Sun et al. [108] proposed an intrusion detection scheme which is able to detect malicious assaults using cloud eye in an IoT-based cloud environment. On the device side, the cloud eye uses an intelligent lightweight agent scanner to detect the malicious data from the incoming data packets. The server side of the cloud eye consists of a large database which can store the predefined attack patterns and is updated periodically. Their system employs Suspicious Bucket Cross Filtering (SBCF) to detect the malicious data from the data packets. The type of attack is identified based on matching patterns with a predefined attack signature pattern. The advantages of this system are that it provides trusted and secured services [109] without compromising privacy. Moreover, the proposed intrusion detection system provides better resource optimization and can efficiently detect attacks with limited predefined pattern signatures. The limitations are that it consumes more memory and has the ability to detect only a limited number of known and familiar attacks based on the pregenerated signature patterns. Table 2 shows the comparison of various IDSs developed based on the signature patterns.Table 2
Comparison of IDS based on signature.
AuthorsMethodologiesAdvantagesLimitationsAmin et al. [105]Network intrusion-based detection system for IP-USN(1) Better false alarm rate(1) Moreover overhead due to redundant data transmission(2) Better intrusion detection accuracy(2) Detects only limited number of assaults(3) Better optimization of network resources(4) Lightweight in natureOh et al. [107]Pattern matching engine based malicious node detection(1) Low computational overhead(1) Detects only limited set of fixed attacks(2) High intrusion detection accuracy with limited pregenerated signatures(2) Real time implementation is not possible(3) Scalable in natureSun et al. [108]Malicious bodes detection system based on cloud eye an antimalware detection system(1) Secured and trusted service with privacy(1) Consumes more memory(2) Better time and space complexity(2) Detects only very few type of attacks(3) Better intrusion detection accuracy


## 4.3. IDS Based on Specification

IDS based on specification detects the intrusion specification normally. The detected intrusions are captured by the legitimate system for further analysis. In the past, many researchers have designed many IDSs based on their specifications to provide enhanced security to the IoT environment. Some of IDSs based on its specification are discussed in this section. Misra et al. [110] planned an IDS with the service oriented architecture (SOA) model in the IoT environment. In this IDS, the SOA is configured to act as middleware to provide the services to the IoT applications. The proposed IDS sends a DALERT control message to all the available nodes. When the requests by the particular devices to middleware exceed the limit which is set as threshold value, the system detects the possibility of vulnerability in the network. Based on this information, the network administrator detects the intrusions. The limitations are it has high false positive rate and it cannot be implemented in the real time. Moreover, this IDS can be able to detect only the intrusions and it is not able to confirm the attack. Murynets and Jover [111] designed an intrusion detection system which is able to detect intrusions from short message sender (SMS) by using volumetric and content-based techniques. The aim of volumetric analysis is to detect the intrusion based on the deviation of the predefined pattern. The main aim of the content-based algorithm is to track the devices in the IoT environment. By muting these two algorithms, the independent DoS attack can be detected efficiently. The advantages of these IDSs are that they provide better intrusion detection accuracy. The limitations are that it has complex implementation tasks and does not have the ability to detect intrusions in real time.Xia et al. [112] designed a new IDS which can identify the node internal attacks in the IoT environment. This IDS has been designed along with a privacy aware routing protocol which ensures the privacy of the nodes in the network. In the route maintenance phase, the malicious activities are detected with the help of neighbour nodes and also based on the traffic analysis of the node’s past behaviour. The advantages are that it has better intrusion detection accuracy and a low expectancy. The limitations are its communication and computation overheads. Moreover, the system can identify only a restricted number of assaults. La et al. [113] proposed an IDS based on innovative model which can detect deceptive assaults. The system employs honeypots as defence tool, which is capable of analysing the incoming data packets based on the predefined intelligent rules. Any suspected data packets are further analysed by the honeypots. The advantage of this IDS is its better intrusion detection accuracy. The limitations are its overhead in the intrusion detection process which consumes more network resources in the IoT environment.Ahmed and Ko [36] designed an IDS which can provide defence against the black hole attack. In local decision procedure, the data about relationships among the nodes is gathered by the neighbours to detect the malicious behaviours. The validity of the malevolent nodes identified at the local decision process is further verified by the global node verification phase. The advantages of this IDS are its real-time intrusion detection and its intrusion detection exactness. Furthermore, the recommended IDS has a better packet delivery ratio and provides good defence against black hole attacks in an IoT environment. The limitations are that it is planned only to detect black hole attacks and has high false positive and low true positive rates. Moreover, the accuracy of IDS decreases as the count of infected nodes increases.Surendar and Umamakeswari [114] planned an IDS which can identify sinkhole assaults using intrusion detection based on a constraint-based specification model with a request-response method in the IoT environment. The observer node plays a vital role by checking the behaviour of all nodes to identify the nodes which drop packets. The advantages of this IDS are its low storage and computational overhead. The limitations are that intrusions are not detected in real time. Moreover, intrusion accuracy is inversely proportional to the total number of infected nodes. Fu et al. [115] proposed an IDS which can detect attacks using an automata model in the IoT environment. The proposed IDS has four main mechanisms, namely, event monitor, event data base, event analyser, and response event. The vital role of the event monitor is to check the activities in the network and transmit them in digital format to the event analyser. The role of the event database is to store the recorded events and differentiate them into normal data and abnormal data. The role of the event analyser is to analyse the stored data, and it works based on three submodules, namely, network structure learning module, action flow abstraction learning module, and intrusion detection module. The benefits of this IDS are its easy implementation and better intrusion detection rate. The limitations are its communications overhead and increased resource consumption. Moreover, this IDS has more delay and can be applied only to delay-tolerant networks.Bose et al. [116] suggested an IDS which is able to detect the selective forwarding attack in the IoT environment. This IDS detects the intrusion in two ways, namely, CIDS and DIDS. In CIDS, the IDS is placed in the sink to detect the malicious nodes. In a distributed IDS, the intrusion detection scheme is located in the routing nodes. In this IDS, the nodes are monitored at two places. The first level of monitoring is carried out by the router nodes, and the second level of monitoring is carried out by the sink. Initially, the router nodes checked the performance of their neighbour and sent it to the sink. The sink cross checks the behaviour of the nodes and identifies the nodes that drop packets frequently. The nodes which release packets frequently are called “malevolent nodes,” and they are isolated from the network. The advantages of this IDS are its easy implementation with less complexity. The limitations are its low intrusion detection accuracy and the fact that it consumes more network resources. Moreover, the proposed IDS has high computation and communication overhead.Liu et al. [117] have designed an IDS which can identify the intrusion. The proposed IDS classifies the data into low-risk and high-risk data. Moreover, SFC and PCA algorithms are employed for self-adjustment in the detection frequency. The advantages of this intrusion detection system are its high adaptiveness and better handling of false data alarm. The limitations are its false positive rate. Moreover, the intrusion detection accuracy decreases when the volume of the data increases by the devices in IoT environment. Table 3 gives the comparison of IDSs developed based on specification.Table 3
Comparison of IDS based on specification.
AuthorsMethodologiesAdvantagesLimitationsMisra et al. [110]SOA-based attack detection based on the system model in IoT environment(1) Better optimization of network resources(1) High true positive and false negative rates(2) Better intrusion detection accurateness(2) Inability to detect the intrusion at the real time(3) Implementation is very complexMurynets and Jover [111]Contact- and volumetric-based intrusion detection for SMS in IoT environment(1) Better intrusion detection rate(1) Implementation is complex(2) Better resource optimization(2) Not a real-time IDSXia et al. [112]An incentive-based internal attack detection mechanism based on neighbour nodes to provide truthful information(1) Better intrusion detection rate(1) Overhead in terms of communication and computation(2) Low delay(2) Can only detect the known number of limited attacks(3) Provides trust-based security during intrusion detectionLa et al. [113]Game theory model-based deceptive attack detection for honeypot-based IoT network(1) Better intrusion detection accuracy(1) High overhead in terms of network resources(2) Real-time-based intrusion detection(2) It has more coverage timeAhmed and Ko [36]Detection of black hole attack using efficient mitigations techniques for RPL networks in IoT environment(1) Real-time intrusion detection with better accuracy(1) This IDS detects only black hole attack(2) Enhancement in packet delivery ratio (PDR)(2) The intrusion accuracy decreases when the number of infected nodes increases(3) Better false positive rate(3) Overhead in terms of communicationSurendar and Umamakeswari et al. [114]Specification based sink hole attack detection in IoT networks(1) Better overhead in terms of storage(1) Intrusion accuracy is indirectly proportional with number of infected nodes(2) Better optimization of network resourcesFu et al. [115]Automata model-based uniform intrusion detection in IoT networks(1) Its easy implementation and real time detection of intrusion(1) High consumption of network resources(2) Less complexity(2) High latency and computational overhead(3) Better intrusion detection accuracy(3) It has high false positive rate when the percentage of infected node increasesBose et al. [116]Sequential probability ratio test-based selective forward attack detection based on probability of packet drop in IoT environment(1) Better true positive rate with easy implementation(1) High consumption of network resources(2) Complexity is low(2) Overhead in terms of communication and has high delay(3) False positive rate is high when the percentage of infected nodes increasesLiu et al. [117]SFC and PCA algorithms-based attack detection in IoT environment(1) Adaptiveness(1) The intrusion detection decreases when the volume of the data increases(2) Better false data alarm(2) Resource consumption is high(3) Better intrusion detection accuracy


## 4.4. IDS Based on the Hybrid Detection Method

The IDSs based on the hybrid method detect the abnormal behaviour of the nodes by combining the IDS based on anomaly detection and signature generation. The capability of the IDS is that it can identify the unknown types of attacks more efficiently which were not detected in IDS-based anomaly detection and signature generation.Various authors have proposed many IDSs based on the hybrid approach to enhance the security of devices in the IoT environment. Amin et al. [118] have designed the security framework which is able to detect attacks in the Internet protocol–ubiquitous sensor network (IP-USN). The proposed framework works on two modules, namely, Internet packet analyser (IPA) and the USN packet analyser. The main aim of these modules is to provide efficient analysis of the incoming data traffic. IPA module is further subdivided into two modules, namely, anomaly detector and pattern classifier. The vital role of the attack detector is to detect the various behaviours of the devices. The packet analyser detects the abnormal behaviour of the devices and maps it to the predefined patterns using a machine learning algorithm and groups them under a common label. The advantages of this IDS are its lightweight property and it has better false error detection rate. Moreover, the proposed IDS has a high positive intrusion detection rate. The limitations are the overhead of computation and the vulnerability to delay during intrusion detection.Kasinathan et al. [119] have designed a security framework which can detect DoS attacks on the 6LoWPAN (IPv6 over Low-Power Private Area Network) in the IoT environment. The proposed framework efficiently analysed the incoming data packets in 6LoWPAN to detect the anomalies in the network. Once the intrusion is detected, the alarm is raised to the decision manager. The DoS protection manager validates the intrusion with preloaded signatures which are stored in the database. If the detected anomaly matches with the predefined digital signatures. The advantage of this IDS is false intrusion detection rate that is decreased and improved availability. The limitations are that this IDS can be implemented only to the network which is operated only in dynamic topology. Moreover, the predefined signatures are not updated frequently by the DoS protection manager.Raza et al. [120] suggested an IDS which can identify the assaults based on routing in the IoT environment. The suggested IDS is designed using three modules. The first module consists of 6LoWPAN mapper; the vital role of the module is to gather the information in the network. The role of the second module is to analyse the collected information and detect intrusions in the network. The third module comprises of distributed firewall; the abnormal activities enter into the network and also handle packet drops in the network. The advantages of it are that this IDS can be extended to other networks. Moreover, the proposed IDS can be implemented in real time and has a high positive intrusion detection rate. The limitations are that it consumes more network resources and has improved communication and computation overhead.Matsunaga and Toyoda [121] designed an IDS which can be able to detect the intrusion based on neighbour node broadcasting the latest rank based on the time stamp method. The system functions in two steps. In the first step, each node broadcasts its ranks to its neighbour nodes. In the second stage, the time stamp has been attached to each node to validate any anomalies. The advantage of this IDS is that it has more true positive intrusion detection rate, and it is highly scalable in nature. The limitations are it has overhead in terms of computation and communication. Moreover, the proposed IDS consumes a lot of resources which affects the network’s consistency.Sedjelmaci et al. [122] suggested an IDS based on a game theory approach with a hybrid intrusion detection method. The proposed IDS uses both anomaly- and signature-based methods to detect intrusions in the network. Nash equilibrium (NE) is computed for all the nodes. The computed NE score and the type of intrusion detection method employed are decoded. The advantage of this IDS is that it has low overhead. Moreover, the system has an improved intrusion detection rate and is lightweight in nature. The limitations are that it has a high latency and complex computational overhead. Shreenivas et al. [123] suggested an IDS where the attacks are detected based on EthereumX (ETX) value and geographical hints. The proposed IDS uses 6mapper to monitor the nodes behaviour in a directed acyclic graph (DAG) based on calculated ETX values, and the intrusion is detected. The advantages of this IDS are its improved intrusion detection accuracy and its real-time implementation. The limitations are its increased false positive intrusion detection accuracy.Midi et al. [124] suggested an IDS which can detect attacks based on expert knowledge driven in the IoT environment. The expert knowledge driven system monitors the network to sense intrusions in the network. The advantages of this IDS are its high intrusion detection accuracy and better optimization of the network resources in terms of RAM and CPU usage. The limitations are its computation overhead which affects the network performance considerably. Sedjelmaci and Senouci [125] suggested an IDS which can detect wormhole and sinkhole attacks based on the game theory model in the IoT environment. The advantages of this IDS are its high intrusion detection accuracy and its lightweight nature. Moreover, the proposed IDS optimizes network resources. The limitations are that it has computational overhead which can affect the network performance considerably. Table 4 gives the comparison of hybrid-based intrusion detection methods.Table 4
Comparison of hybrid-based intrusion detection methods used in the IoT environment.
AuthorsMethodologiesAdvantagesLimitationsAmin et al. [118]Detection of attacks using generalised architecture for IP-USN in IoT environment(1) Better false intrusion detection rate(1) Overhead in terms of computation(2) Better optimization of network resources(2) High latencyKasinathan et al. [119]Detection of abnormal behaviour of nodes and their matching signature using DoS protection manger(1) IDS based on real time(1) Works only with the network with dynamic tropology(2) Better false intrusion detection alarm(2) The pregenerated signatures are not frequently updated by DoS protection manager(3) Better resource optimization1Raza et al. [120]Detection of routing attacks based on integrated mini firewall-based anomaly detection and distributed firewall-based signature generation IDS in IoT environment(1) Intrusion detection in real time(1) Resource consumption by the network is high(2) Overhead is minimal(2) False intrusion detection rate is high(3) Better intrusion detection accuracyMatsunaga and Toyoda [121]Detection of attacks based timestamp to detect the inconsistency of nodes during broadcast of rank to the neighbour nodes(1) Better overhead(1) High consumption of network resources(2) False data intrusion detection is low(2) High overhead in terms of computation(3) Better intrusion detection accuracySedjelmaci et al. [122]Game theory-based effective attack detection using anomaly and signature detection techniques for IoT networks(1) Better intrusion detection accuracy(1) High latency(2) Computational overhead is high(2) Better false intrusion detection rate(3) Network resources are not optimized(3) Energy consumption by the nodes are highShreenivas et al. [123]ETX metric and geographical hints-based attack detection in IoT(1) Intrusion detection in real time(1) High false positive intrusion detection rate(2) Better power optimization(2) Overhead in terms of computation and communicationMidi et al. [124]Knowledge driven expect real time-based IDS for self-adapting IoT networks(1) Better intrusion detection accuracy(1) Overhead in terms computation and communication(2) Better utilization network and system resources(3) Low false intrusion detection rateSedjelmaci and Senouci [125]Game theory-based worm hole, sink hole and black hole attack detection in IoT environment(1) Better intrusion detection accuracy(1) Overhead in terms computation and communication(2) Delay is minimal(3) Network resource consumption is low


## 5. Proposed Model

This paper consists of two components, namely, the survey of related works and the proposed work. For the effective evaluation of the existing and proposed systems, the following network set up has been used. The simulation parameters are shown in Table5.Table 5
Simulation parameters.
Simulation parametersParameter nameParameter valueNetwork area (m2)500 m × 500 mNo. of sensor nodes50–500 nodesBasic routing protocolsLEACH protocol and AODV protocolMobility model (for mobile scenario)Random way point mobility modelTransport layer protocolsTCP and UDPEnergy of nodes2 J per nodeInitial energy0.5 JPacket size1024 bitsEelec50 nJ/bitThe overview of the intelligent IDS developed in this work is shown in Figure2. The developed model consists of 7 major modules, namely, IoT IDS dataset, an administrator module, a data preprocessing module, a classification module, a decision manager, a fuzzy inference system, and a knowledge base. In this work, the KDD cup 1999 dataset that consists of 41 features has been used for developing the proposed system. The administrator module can be used as a user interface and also acts as the intrusion prevention module whenever the decision manager provides reports on intrusion and intruders. The data preprocessing module contains two submodules such as the feature analysis subsystem and the feature selection subsystem. The feature analysis subsystem computes the strengths of each of the 41 attributes present in the dataset and provides a measured score in the form of an information gain ratio. The feature selection subsystem selects the most contributing features with the help of the feature analysis subsystem and the fuzzy inference system. The selected features are given as feedback attributes to the classification module. The classification module analyses the features on its own and compares them with the features selected by the data preprocessing module. It applies fuzzy rules with the help of the fuzzy inference system and the decision. The decision manager has complete control over the system.Figure 2
Architecture of the proposed system.Therefore, the decision manager coordinates with all the other modules and makes the final decision on intrusions and notifies the intrusions and also about the intruders to the administrator module for taking actions including preventing the users and nodes from participation in the communication. The knowledge base present in this work consists of domain rules, general rules, and fuzzy rules which are used by the decision manger and classification module for making most efficient decisions.


### 5.1. Intelligent Feature Selection

The intelligent feature selection procedure [126] used in this work computes the information gain ratio for all the features present in the dataset. It uses a threshold for choosing the most subsidizing features based on the sensitivity of the information that is communicated through the IoT network. Based on the threshold and the information gain ratio and the usage history of users, the feature selection algorithm selects the optimal number of features for the particular application over the specified duration of time (Algorithm 1).Algorithm 1: Intelligent fuzzy rule-based feature selection algorithm.
Input: Intrusion detection Dataset for IDS, 41 features existent in the dataset from set, Fuzzy rules and Threshold (Thres).Output: Selected features with ranksStep 1: Initialize number of features NFS = {}Step 2: Read the IDS dataset (IoT _DS), feature Set (FS1, FS2…….FS41), fuzzy Rules (FR1, FR2…..FRn), Thres.N = 71Step 3: Fori = 1 to N do//finding the information gain ratio for the 71 featuresBeginSplit (IoT dataset, AS1, AS2,…….ASN, G1,G2)j = i + 1;Compute IGR values for all features (As1, AS2,……..ASN) using the formulasInfo (G1) = −∑j=1mfreqASj,G1/G1log2freqASj,G1/G1Info (G2) =∑i=1nGi/G2∗infoGiIGR (ASi) =  InfoG1−InfoG2/InfoG1+InfoG2∗ 100If IGR (ASi) ≥ Thres thenFS = FS U ASi;End IfStep 4: Apply Fuzzy rules.Step 6: Check features again using fuzzy rules and find the important features.Return selected feature set.Table6 shows the extracted dataset after applying the intelligent feature selection algorithm. Initially, all 41 attributes [127] from the IDS dataset were considered.Table 6
Selected features list from the IDS dataset.
S. No.Feature numberFeature name12protocol_type24src_byte38wrong_fragment414root_shell515su_attempted619num_access_shells727diff_srv_rate829srv_serror_rate931srv_diff_host_rate1032dst_host_count1135dst_host_diff_srv_count1236dst_host_same_src_port_rate1337dst_host_srv_diff_host_rate1438dst_host_serror_rateThese features are given as feedback to the deep fuzzy CNN proposed in this work. Moreover, the fuzzy CNN compares the given features with other features selected by it and applies fuzzy rules to find an optimal set of features. Finally, the optimal set of features is used by the fully connected network component of the fuzzy CNN for performing the classification, whose results are used to identify the intrusions more accurately.


### 5.2. Fuzzy Inference System

The fuzzy inference system consists of fuzzy rules, marching, rule firing, and rule execution components. In rule matching [128], first it performs fuzzification using a triangular membership function for all the attributes, and then, it forms the fuzzy rules. The decision manager takes the input from the fuzzy inference rules and executes it to take the decision. The sample set of fuzzy rules is given in Table 7.Table 7
Fuzzy inference rules.
Flow typeLabelAttack typeNo. of packets droppedDecisionLowAnomalyDoSHighAttackedLowAnomalyProbeHighAttackedLowAnomalyL2RMediumAttackedMediumNormalHTTP floodingMediumAttackedHighAnomalyR2LLowAttackedMediumNormalUDP floodingMediumAttackedHighAnomalyARP floodingLowAttackedHighNormalDoS-synfloodingHighAttackedHighNormalNormalLowNot attackedHighAnomalyDoSHighAttackedLowAnomalyL2RLowAttackedHighNormalNormalMediumNot attackedLowAnomalyNormalLowNot attackedHighAnomalyR2LLowAttacked


### 5.3. Classification Algorithm

The new intelligent deep classification algorithm proposed and designed in this work is accomplished by using the CNN algorithm with IF…THEN rules. This deep fuzzy classifier performs both convolution and max pooling operations, where the convolution for two functionsf and g is represented for the operator t using the integral given in the following equation as follows:(1)f∗gt=∫−∞∞fτgt−τdτ=∫−∞∞ft−τgτdτ.Moreover, we used 9 max pooling layers and 10 convolution layers that are integrated with a fuzzy inference system for performing the classification task. All these layers together are operating on the dataset and providing the set of features to be used for classification.(2)fx=x+1x.As the bias function in the fully connected network component of the proposed fuzzy CNN. If both are matching, it proceeds with the classification process. In cases of mismatch, it consults with the decision manager to provide feedback on the attributes to be used based on the sensitiveness of the attributes.


## 5.1. Intelligent Feature Selection

The intelligent feature selection procedure [126] used in this work computes the information gain ratio for all the features present in the dataset. It uses a threshold for choosing the most subsidizing features based on the sensitivity of the information that is communicated through the IoT network. Based on the threshold and the information gain ratio and the usage history of users, the feature selection algorithm selects the optimal number of features for the particular application over the specified duration of time (Algorithm 1).Algorithm 1: Intelligent fuzzy rule-based feature selection algorithm.
Input: Intrusion detection Dataset for IDS, 41 features existent in the dataset from set, Fuzzy rules and Threshold (Thres).Output: Selected features with ranksStep 1: Initialize number of features NFS = {}Step 2: Read the IDS dataset (IoT _DS), feature Set (FS1, FS2…….FS41), fuzzy Rules (FR1, FR2…..FRn), Thres.N = 71Step 3: Fori = 1 to N do//finding the information gain ratio for the 71 featuresBeginSplit (IoT dataset, AS1, AS2,…….ASN, G1,G2)j = i + 1;Compute IGR values for all features (As1, AS2,……..ASN) using the formulasInfo (G1) = −∑j=1mfreqASj,G1/G1log2freqASj,G1/G1Info (G2) =∑i=1nGi/G2∗infoGiIGR (ASi) =  InfoG1−InfoG2/InfoG1+InfoG2∗ 100If IGR (ASi) ≥ Thres thenFS = FS U ASi;End IfStep 4: Apply Fuzzy rules.Step 6: Check features again using fuzzy rules and find the important features.Return selected feature set.Table6 shows the extracted dataset after applying the intelligent feature selection algorithm. Initially, all 41 attributes [127] from the IDS dataset were considered.Table 6
Selected features list from the IDS dataset.
S. No.Feature numberFeature name12protocol_type24src_byte38wrong_fragment414root_shell515su_attempted619num_access_shells727diff_srv_rate829srv_serror_rate931srv_diff_host_rate1032dst_host_count1135dst_host_diff_srv_count1236dst_host_same_src_port_rate1337dst_host_srv_diff_host_rate1438dst_host_serror_rateThese features are given as feedback to the deep fuzzy CNN proposed in this work. Moreover, the fuzzy CNN compares the given features with other features selected by it and applies fuzzy rules to find an optimal set of features. Finally, the optimal set of features is used by the fully connected network component of the fuzzy CNN for performing the classification, whose results are used to identify the intrusions more accurately.


## 5.2. Fuzzy Inference System

The fuzzy inference system consists of fuzzy rules, marching, rule firing, and rule execution components. In rule matching [128], first it performs fuzzification using a triangular membership function for all the attributes, and then, it forms the fuzzy rules. The decision manager takes the input from the fuzzy inference rules and executes it to take the decision. The sample set of fuzzy rules is given in Table 7.Table 7
Fuzzy inference rules.
Flow typeLabelAttack typeNo. of packets droppedDecisionLowAnomalyDoSHighAttackedLowAnomalyProbeHighAttackedLowAnomalyL2RMediumAttackedMediumNormalHTTP floodingMediumAttackedHighAnomalyR2LLowAttackedMediumNormalUDP floodingMediumAttackedHighAnomalyARP floodingLowAttackedHighNormalDoS-synfloodingHighAttackedHighNormalNormalLowNot attackedHighAnomalyDoSHighAttackedLowAnomalyL2RLowAttackedHighNormalNormalMediumNot attackedLowAnomalyNormalLowNot attackedHighAnomalyR2LLowAttacked


## 5.3. Classification Algorithm

The new intelligent deep classification algorithm proposed and designed in this work is accomplished by using the CNN algorithm with IF…THEN rules. This deep fuzzy classifier performs both convolution and max pooling operations, where the convolution for two functionsf and g is represented for the operator t using the integral given in the following equation as follows:(1)f∗gt=∫−∞∞fτgt−τdτ=∫−∞∞ft−τgτdτ.Moreover, we used 9 max pooling layers and 10 convolution layers that are integrated with a fuzzy inference system for performing the classification task. All these layers together are operating on the dataset and providing the set of features to be used for classification.(2)fx=x+1x.As the bias function in the fully connected network component of the proposed fuzzy CNN. If both are matching, it proceeds with the classification process. In cases of mismatch, it consults with the decision manager to provide feedback on the attributes to be used based on the sensitiveness of the attributes.


## 6. Analysis of Existing IDS Approaches

In this section, the performance analysis of various existing intrusion detection approaches [129–136] is based on performance metrics like intrusion detection accuracy (IDA), false positive intrusion detection rate (FPIDR), real time intrusion detection (RTID), fault tolerance rate (FTR), and network resource optimization (NRO) scalability. Table 8 gives the performance investigation of different intrusion detection approaches with the given performance metrics.Table 8
Performance investigation of different intrusion detection approaches.
IDS typeAuthorsIDAFPIDRRTIDFTRNROScalabilityIDS based on anomaly detectionFu et al. [91]√×√√×√Ding et al. [92]√×××√×Rajasegarar et al. [93]√×××××Chen et al. [94]××√√×√Ham et al. [95]√√××××Wang et al. [96]√√×√××Pongle and Chava [33]××√×√×Carventes et al. [97]√√×××√Thangaramya et al. [23]××××√×Grgic et al. [101]√×√×√×Sonar &Upadhyay [102]√√××××Hudo et al. [104]√√√×××IDS based on signatureAmin et al. [105]√√×√××Sun et al. [108]√×√×××Oh et al. [107]××√××√IDS based on specifica tionMisra et al. [110]×××√××Murynets and Jover [111]√×××√×Xia et al. [112]√×√×××La et al. [113]√×√×××Ahmed and Ko [36]√√√×××Surender and Umamakeswari [114]√√√×√×Fu et al. [115]××√√××Gara et al. [30]√×××××Liu et al. [117]××√√××IDS based on the hybrid methodAmin et al. [118]×√××√×Kasinathan et al. [119]√√√×××Raza et al. [120]××√×××Matsunaga and Toyoda [121]√√√√××Shreenivas et al. [123]√×√×√×Midi et al. [124]√√√×√×Sedjelmaci and Senouci [125]√√√×√×Figure3 gives the performance of various categories of intrusion detection systems with various performance metrics like intrusion detection rate (IDR), real time intrusion detection (RTID), fault tolerance rate (FTR), and network resource optimization (NRO) scalability.Figure 3
Performance analysis of various intrusion detection systems.As shown in Figure3, IDS based on anomaly detection has a better false positive intrusion detection rate when compared with other categories IDS in the IoT environment. The reason for this improvement is that IDS based on anomaly detection monitors the data effectively to detect the irregular behaviour of the nodes and detect anomalies if there is a deviation from the normal behaviour. Hence, the IDS based on anomaly detection has better false positive rate. The percentage of intrusion detection accuracy and network resource optimization of IDS based on signatures is much higher compared with other IDS in the IoT environment. The reason for this performance is that an IDS based on signature efficiently monitors the incoming data to detect any anomalies. The detected anomalies are compared with the predefined signature attacks which are generated by the network administrator. If the detected anomalies match a predefined attack signature, the type of attack is detected. Moreover, the IDS based on signatures has better optimization of network resources. IDS based on the specification has higher real time intrusion detection system since most of the IDS are designed based on generic frame work which detects the intrusion efficiently. An ID based on a hybrid detection method has better scalability and is fault-tolerant by nature.


### 6.1. Simulation Results of the Proposed Work

The proposed system is executed in the NS3 simulator. The suggested system is compared with other prevailing systems using the performance parameters, namely, packet delivery ratio, delay, average energy consumption, network life time, DoS attack, probe attack, L2R attack, R2L attack, and finally security analysis. Figure4 provides the comparative analysis based on packet delivery ratio (PDR) between the proposed IDS and the other three existing systems on the IDS.Figure 4
Packet delivery analysis.From Figure4, it is clear that the proposed IDS has a better PDR when equated with other related IDS works by Ding et al. [92], Chen et al. [94], and Wang et al. [96]. This improvement is possible because the proposed system uses the feedback from the results of the intelligent feature selection algorithm to select the dominant features based on information gain. Finally, it uses fuzzy-based CNN classifier to identify intrusions. Hence, the proposed system has a better PDR.From the graph in Figure5, we could prove that the proposed intelligent fuzzy CNN classifier presents a lower communication delay because it has better attack detection accuracy and efficiently identifies the malevolent nodes. Hence, it has better performance in communication delay. Figure 6 provides a comparative analysis of energy consumption in the network with other existing systems.Figure 5
Delay analysis.Figure 6
Energy consumption analysis.Figure6 shows the proposed IDS consumes less energy in comparison with existing works such as the systems proposed by Ding et al. [92], Chen [94], and Wang [96] because the proposed system uses only a selected and optimal number of features and makes the classifier converge fast. Hence, the suggested system has optimal energy consumption than the other existing systems.From the graph shown in Figure7, we can understand that the proposed intelligent classifier has better network life-time analysis because it has better attack detection accuracy and efficiently identifies the anomaly. In the proposed work, the energy spent on communicating the packets sent by malicious nodes is eliminated, and hence, it increased the network lifetime as well. Figure 8 provides analysis in DoS detection accuracy of the proposed system with other existing approaches. From Figure 8, the proposed fuzzy CNN can detect DoS attacks more efficiently due to the use of fuzzy rules.Figure 7
Analysis of network lifetime.Figure 8
Analysis of DoS attack detection.Figure9 provides an analysis of probe attack detection in the network. The proposed fuzzy CNN detects the probe attacks more accurately with deductive inference.Figure 9
Analysis of probe attack detection.Figure10 provides the analysis of R2L attack for the proposed IDS with other existing systems.Figure 10
Analysis of R2L attack detection.From Figure10, the proposed fuzzy CNN has more than 5% detection accuracy in comparison to existing systems. The use of a new bias function along with more classes of fuzzy rules enabled the proposed fuzzy CNN classifier to detect R2L attacks more reliably than the related classifier. Figure 11 provides the analysis of the overall security provided after applying the IDSs in the network based on comparisons.Figure 11
Security analysis of the proposed system.From Figure11, the overall security provided by the proposed IDS higher than the security provided by the related IDSs tested in the IoT. The security is increased since the proposed system identified the intruders more accurately and prevented the malicious nodes in the network communication of IoT.Based on the survey made in this work, the following recommendations are provided:(1)
The hybrid intrusion detection systems are better candidates for providing security to the IoT environment since they detect identifiable and unidentifiable attacks(2)
In real-time IoT security environment, AI and ML-based techniques with an anomaly intrusion detection model can be deployed(3)
A secured routing algorithm must be developed by including the IDS component in the nodes to provide highly secured communication(4)
Feature selection and feature optimization tasks must be carried out to develop IDSs with a higher detection rate(5)
Rule optimization must be performed to reduce the detection timeTo verify the usefulness of these suggestions, they were implanted into the new IDS developed in this work and tested. Based on the experimental verification, this survey provided the guidelines for the development of intelligent IDS for IoT.


## 6.1. Simulation Results of the Proposed Work

The proposed system is executed in the NS3 simulator. The suggested system is compared with other prevailing systems using the performance parameters, namely, packet delivery ratio, delay, average energy consumption, network life time, DoS attack, probe attack, L2R attack, R2L attack, and finally security analysis. Figure4 provides the comparative analysis based on packet delivery ratio (PDR) between the proposed IDS and the other three existing systems on the IDS.Figure 4
Packet delivery analysis.From Figure4, it is clear that the proposed IDS has a better PDR when equated with other related IDS works by Ding et al. [92], Chen et al. [94], and Wang et al. [96]. This improvement is possible because the proposed system uses the feedback from the results of the intelligent feature selection algorithm to select the dominant features based on information gain. Finally, it uses fuzzy-based CNN classifier to identify intrusions. Hence, the proposed system has a better PDR.From the graph in Figure5, we could prove that the proposed intelligent fuzzy CNN classifier presents a lower communication delay because it has better attack detection accuracy and efficiently identifies the malevolent nodes. Hence, it has better performance in communication delay. Figure 6 provides a comparative analysis of energy consumption in the network with other existing systems.Figure 5
Delay analysis.Figure 6
Energy consumption analysis.Figure6 shows the proposed IDS consumes less energy in comparison with existing works such as the systems proposed by Ding et al. [92], Chen [94], and Wang [96] because the proposed system uses only a selected and optimal number of features and makes the classifier converge fast. Hence, the suggested system has optimal energy consumption than the other existing systems.From the graph shown in Figure7, we can understand that the proposed intelligent classifier has better network life-time analysis because it has better attack detection accuracy and efficiently identifies the anomaly. In the proposed work, the energy spent on communicating the packets sent by malicious nodes is eliminated, and hence, it increased the network lifetime as well. Figure 8 provides analysis in DoS detection accuracy of the proposed system with other existing approaches. From Figure 8, the proposed fuzzy CNN can detect DoS attacks more efficiently due to the use of fuzzy rules.Figure 7
Analysis of network lifetime.Figure 8
Analysis of DoS attack detection.Figure9 provides an analysis of probe attack detection in the network. The proposed fuzzy CNN detects the probe attacks more accurately with deductive inference.Figure 9
Analysis of probe attack detection.Figure10 provides the analysis of R2L attack for the proposed IDS with other existing systems.Figure 10
Analysis of R2L attack detection.From Figure10, the proposed fuzzy CNN has more than 5% detection accuracy in comparison to existing systems. The use of a new bias function along with more classes of fuzzy rules enabled the proposed fuzzy CNN classifier to detect R2L attacks more reliably than the related classifier. Figure 11 provides the analysis of the overall security provided after applying the IDSs in the network based on comparisons.Figure 11
Security analysis of the proposed system.From Figure11, the overall security provided by the proposed IDS higher than the security provided by the related IDSs tested in the IoT. The security is increased since the proposed system identified the intruders more accurately and prevented the malicious nodes in the network communication of IoT.Based on the survey made in this work, the following recommendations are provided:(1)
The hybrid intrusion detection systems are better candidates for providing security to the IoT environment since they detect identifiable and unidentifiable attacks(2)
In real-time IoT security environment, AI and ML-based techniques with an anomaly intrusion detection model can be deployed(3)
A secured routing algorithm must be developed by including the IDS component in the nodes to provide highly secured communication(4)
Feature selection and feature optimization tasks must be carried out to develop IDSs with a higher detection rate(5)
Rule optimization must be performed to reduce the detection timeTo verify the usefulness of these suggestions, they were implanted into the new IDS developed in this work and tested. Based on the experimental verification, this survey provided the guidelines for the development of intelligent IDS for IoT.


## 7. Conclusion and Future Challenges

In this paper, we provide a detailed analysis of IDS developed in the IoT environment that are presented by various researchers. In addition, we proposed a new intelligent IDS using fuzzy CNN to overcome the limitations of the IDSs present in the literature. The IDS in IoT presented in this paper was subdivided into four categories, namely, IDS based on anomaly detection, signature, specifications, and hybrid method for performing the comparative analysis. Under each category, an in-depth analysis of various existing IDS protocols is carried out. Moreover, the performance analysis of each category of IDS are carried out based on various performance metrics like network resource optimization, false positive intrusion detection rate, and scalability. Finally, the intelligent IDS which is developed in this work utilizes information gain ratio for selecting prominent features. For intrusion classification, the intelligent fuzzy-based CNN classifier is employed to accurately classify the intrusion based on QoS parameters. The proposed intelligent classifier is simulated using the NS3 simulator, and it is compared with the performance metrics, namely, packet delivery ratio, delay, average energy consumption, network life time, DoS attack, probe attack, L2R attack, R2L attack, and finally security analysis. The proposed system is enhanced by security by more than 10%, the network life time by more than 5% and the detection accuracy by 4% than the existing works. The future plan of the suggested system is to apply the proposed intelligent IDS to an IoT-based network with dynamic network topology.



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*Source: 8981988-2023-01-27.xml*