# A Hybrid Model for Intrusion Detection in IoT Applications

Authors: Mohammed I. Alghamdi
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Internet of Things (IoT) networks has recently become an important component of smart cities, smart buildings, health care, and other applications. It finds it beneficial due to the inherent characteristics of low cost, compact, and low-powered IoT devices. At the same time, security remains a challenging issue in the design of IoT networks. Intrusion detection systems (IDS) can be used to identify the occurrence of intrusions in the network, i.e., abnormal activities in the network. The latest advances in machine learning (ML) and metaheuristics can be employed to design effective IDS models for IoT networks. This article develops a novel political optimizer with cascade forward neural network (PO-CFNN-)-based IDS in the IoT environment. The major intention of the PO-CFNN technique is to determine the occurrence of intrusions from the IoT environment. The PO-CFNN technique follows three major processes, namely, preprocessing, classification, and parameter optimization. Initially, the networking data is preprocessed to transform it into a useful format. Following that, the CFNN technique is employed for the identification and classification of intrusions in the IoT environment. In the final stage, the PO algorithm is applied for the optimal adjustment of the parameters involved in the CFNN model. The experimental validation of the PO-CFNN technique on a benchmark dataset stated the better outcomes of the PO-CFNN technique over recent approaches.


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## Body

## 1. Introduction

In the digital era, the Internet of Things (IoT) is known as the most interesting advancement. The web permits associated gadgets to develop dramatically each day, and it has been suggested that more than 75 billion Internet of Things (IoT)-connected devices to be in use by 2025; according to current projections as of 2019, the installed base of IoT devices had grown by approximately thrice [1, 2]. The IoT innovation’s motivation is to interconnect all objects to make all PCs smarter and make it safer to speak with people. Sensors and organizations permit everything to speak with one another straightforwardly to trade basic data [2]. Later on, it is conceivable through machine-to-machine (M2M) correspondence [3]. Various down-to-earth IoT applications can be utilized practically in many fields, like shrewd city applications (brilliant homes, savvy networks, medical services, and others), where those applications work on personal satisfaction [4]. The idea of the intrusion identification framework (IDS) aims to distinguish a danger or intrusion into the organization, and it effectively tracks the organization by recognizing likely occasions and logging data about them by halting episodes. The intrusion detection and prevention system (IDPS), which is a mix of two frameworks used to screen events happening in an organization and assess them for potential infringements or occurrences in security strategies, is the most common way of performing intrusion recognition and stopping to identify episodes [5]. Involving the IoT framework in numerous application areas like medical services, smart homes, shrewd industry, nature observing, and others gives critical advantages to the IoT framework. IoT security issues are a huge concern, which are classification, honesty, accessibility, and approval [6]. The combination of true articles with IoT, in any case, raises the scope of cybersecurity dangers every day.Cyberspace is a fairly weak foundation, not intended to do what it does today, and on which increasing usefulness is constructed [7]. The way that the web is utilized for a wide range of basic activities at the level of people, firms, associations, and even countries has drawn in a wide range of malevolent activities. Cyberattacks can affect a wide range of structures. The issue is how to treat them. For some different types of assault, discovery is an issue and, here and there, the primary issue. Part of the problem is that intrusion detection systems (IDS) are used to alert clients or organizations that they are under attack, or, as in the case of web applications, may not include any malware, but it depends on how a convention is handled [8].Involving AI in intrusion discovery is not new. To be sure, it is now many years old, nearly as old as the field of intrusion detection, for example. Today, AI is not utilized strongly in intrusion identification. It is self-evident that AI could work on fundamentally improving the presentation of IDS, but what is subtler is how to operationalize this thought. There are a few explanations behind that [9]. The main one is that AI is a troublesome subject, a long way from being an adult and just security individuals appear to be keen on involving AI in intrusion identification. Individuals associated with AI appear to be substantially more intrigued by different applications, albeit in numerous ways that cybersecurity should be a characteristic space of utilization for AI [10]. The issue might lie more with cybersecurity than with the AI group. Cybersecurity extends the impression of a turbulent world without intelligence and lacking codification [11].The authors in [12] presented several kinds of attacks in the IoT environment, and a distributed denial-of-service (DDoS) is a vulnerable attack. Blockchain (BC) is used to design a model for secure IoT networks and is employed for cryptocurrency transactions. A new BIoTIDS technique is derived to identify the existence of intrusions by the use of BC. It can determine the occurrence of intrusions from the IoT networks and detect DDoS attacks. Sarhan et al. [13] proposed and evaluated a standard NIDS feature set depending upon NetFlow network metadata gathering status. A set of two NetFlow enabled feature set versions are employed. Next, the authors in [14] proposed a novel feature selection for IDS by the use of information gain (IG) and gain ratio (GR) with the top 50% of features to detect DoS as well as DDoS attacks. The presented model has obtained an optimal subset of features by the use of insertion and union operations on subsets offered by the top 50% of IG and GR features. The authors in [15] presented a novel unified IDS model for the IoT environment for securing the network from various kinds of attacks, such as exploits, DoS, probes, and generics.This article develops a novel political optimizer with cascade forward neural network (PO-CFNN-)-based IDS in the IoT environment. The major intention of the PO-CFNN technique is to define the occurrence of intrusions in the IoT environment. The PO-CFNN approach follows three major processes, namely, preprocessing, classification, and parameter optimization. Initially, the networking data is preprocessed to transform it into a useful format. Following that, the CFNN technique is employed for the identification and classification of intrusions in the IoT environment. In the final stage, the PO algorithm is applied for the optimal adjustment of the parameters involved in the CFNN technique. The experimental validation of the PO-CFNN technique on a benchmark dataset stated the better outcomes of the PO-CFNN technique over recent approaches.


## 2. The Proposed Model

In this study, a novel PO-CFNN approach has been developed for the identification and classification of intrusions in the IoT environment. The PO-CFNN technique follows three major processes, namely preprocessing, classification, and parameter optimization. Firstly, the networking data is preprocessed to transform it into a useful format. Then, the CFNN technique is employed for the identification and classification of intrusions in the IoT environment. In the end, the PO algorithm is used to find the best way to change the parameters in the CFNN model. CFNM-based Intrusion Detection and Classification.During this phase, the CFNN approach was utilized for the identification and classification of intrusions in the IoT environment [16, 17]. The perceptron connection has been developed between input and output through the process of straight linking. Nonetheless, the FFNN connection intended between input and output was an ambiguous link. The linking is nonlinear in shape using an activation function from the hidden state. The system with an ambiguous link between the output and input layer was intended once the association process on perceptron and multilayer systems was collective. The system intended by this technique is called cascade forward neural network (CFNN).
(1)y=∑i=1nfiωiixi+fo∑j=1kωjofjh∑i=1nωjihxi,whereas fi indicates the activation function in the input to output layers as well as ωii characterizes the weight from the input to output layers. The activation function and bias and input layer from the hidden layer denote efh:
(2)y=∑i=1nfiωiixi+foωb+∑j=1kωjofhωjb+∑i=1nωjihxi.Here, the CFNN model was executed in time sequence information. Hence, the neuron from the input layer is intervals of time sequenceXt−1,Xt−2,⋯,Xt−p; however, the output is the existing information Xt.Consider that going to weight vectorω of length s represents the collection of network weights and the objective function as e=1/2Xt−X∧2.DescribedQ was a positive matrix of size s×s, whereas QT=Q. The stage of the method for conjugate gradient optimization is defined below:Setk=0, and choose the primary point Ω0.Estimate the weight gradient.(3)g0=∂e∂ω0=∂e∂ωω=ωo=∂e∂ω10∂e∂ωs0T.Onceg0=0 afterward stop, and it gained an optimum weight =Ω0. Or else, set d0=g0.Estimateαk=argminα≥0eωk+αdk=−gkTdk/dkTQdk.EstimateΩk+1=Ωk+αkdk.Estimategk+1=∂e/∂ωk+1, if gk+1=0 stop and the optimal weight is ωk+1.Estimate(4)βk=gk+1TQdkdkTQdkEstimatedk+1=−gk+1+αkdk.k=k+1; go to step 3.As for the FFNN, the iteration method to weight searching on CFNN is typically termed as an epoch. Assume that the highest amount of epochs representK. Once the iteration termination criteria could not be occurred, still the epochk=K, and subsequently, the iteration method is ended. Substituting the procedure of Qdk with another procedure such as the Hestenes-Stiefel model, especially, the technique Qdk is substituted with gk+1−gk/αk.
(5)βk=gk+1Tgk+1−gkdkTgk+1−gk.For determining the optimum weight of the CFNN, the PO approach is employed and in that way enhances the classifier accuracy.


### 2.1. PO-Based Parameter Optimization

Finally, the PO algorithm is applied for optimal adjustment of the parameters contained in the CFNN model [18, 19]. PO approach is encouraged by b, the western political optimization algorithm which comprises 2 features. The primary statement that all the citizens try to enhance their helpfulness to win the election. PO has been comprised of 5 phases, namely, election campaign, party switching, interparty election, parliamentary affairs, party establishment, and constituency apportionment. The entire population is divided into n political parties, as follows:
(6)P=P1,P2,P3,⋯,Pn.All the parties includen party members that are given as
(7)Pi=pi1,pi2,pi3,⋯,pin.All the party members induced dimension as shown as
(8)pij=pi,1j,pi,2j,pi,3j,⋯,pi,djT.Al solutions are election candidates. Considern electoral district as follows:
(9)C=C1,C2,C3,⋯,Cn.Assume there is an member in all the constituencies.
(10)cj=p1j,p2j,p3j,⋯,pnj.The party leader is described by the member as optimum fitness.(11)q=argmin1≤j≤nfp1j,∀iε1,⋯,n,(12)pi∗=piq.All the party leaders are shown as(13)P∗=p1∗,p2∗,p3∗,⋯,pn∗.The winner of the constituency is named a member of parliament.(14)C=c1∗,c2∗,c3∗,⋯,cn∗.It is possible to split the population intonconstituencies, and each constituency has npolitical parties, where n=Pop.Size as seen in Figure 1.Figure 1
PO logical division of the population.In the election campaign stage, the below equations are utilized to update the position of the probable solution.(15)pi,kjz+1=ifpi,kjz−1≤pi,kjz≤m∗orpi,kjz−1≥pi,kjz≥m∗,m∗+rm∗−pi,kjz;ifpi,kjz−1≤m∗≤pikjzorpi,kjz−1≥m∗≥pi,kjz,m∗+2r−1m∗−pi,kjt;ifm∗≤pi,kjz−1≤pi,kjzorm∗≥pi,kjz−1≥pi,kjz,m∗+2r−1m∗−piklz−1.(16)pi,kjz+1=ifpi,kjz−1≤pi,kjz≤m∗orpi,kjz−1≥pi,kjz≥m∗,m∗+2r−1m∗−pi,kjz;ifpi,kjz−1≤m∗≤pi,kjzorpi,kjz−1≥m∗≥pi,kjz,pi,kjz−1+rpi,kjz−pi,kjz−1;ifm∗≤pi,kiz−1≤pi,kjzorm∗≥pi,kjz−1≥pi,kjz,m∗+2r−1m∗−pi,kjz−1.To balance exploitation, party switch is adopted [20]. Adaptive variable λ is applied; that is drastically minimized from 1 to 0 in the entire iteration method. All the candidates are chosen as per the possibility λ as well as substituted with the worst member of the arbitrarily chosen party.
(17)q=argmaxi≤j≤nfpij.During the election stage, the winner in the constituency is gained as follows:(18)q=argmini≤j≤nfpij,(19)cj∗=pqj.Figure2 shows how PO updates the location of CFNNs. The position of the global optimum is shown by the star symbol. With regard to the party leader, dotted arrows show the position updating of party 3 members, and plain arrows show the position updating of party 3 members in relation to the constituency winners.Figure 2
Depiction of the position updating of CFNNs through PO.


## 2.1. PO-Based Parameter Optimization

Finally, the PO algorithm is applied for optimal adjustment of the parameters contained in the CFNN model [18, 19]. PO approach is encouraged by b, the western political optimization algorithm which comprises 2 features. The primary statement that all the citizens try to enhance their helpfulness to win the election. PO has been comprised of 5 phases, namely, election campaign, party switching, interparty election, parliamentary affairs, party establishment, and constituency apportionment. The entire population is divided into n political parties, as follows:
(6)P=P1,P2,P3,⋯,Pn.All the parties includen party members that are given as
(7)Pi=pi1,pi2,pi3,⋯,pin.All the party members induced dimension as shown as
(8)pij=pi,1j,pi,2j,pi,3j,⋯,pi,djT.Al solutions are election candidates. Considern electoral district as follows:
(9)C=C1,C2,C3,⋯,Cn.Assume there is an member in all the constituencies.
(10)cj=p1j,p2j,p3j,⋯,pnj.The party leader is described by the member as optimum fitness.(11)q=argmin1≤j≤nfp1j,∀iε1,⋯,n,(12)pi∗=piq.All the party leaders are shown as(13)P∗=p1∗,p2∗,p3∗,⋯,pn∗.The winner of the constituency is named a member of parliament.(14)C=c1∗,c2∗,c3∗,⋯,cn∗.It is possible to split the population intonconstituencies, and each constituency has npolitical parties, where n=Pop.Size as seen in Figure 1.Figure 1
PO logical division of the population.In the election campaign stage, the below equations are utilized to update the position of the probable solution.(15)pi,kjz+1=ifpi,kjz−1≤pi,kjz≤m∗orpi,kjz−1≥pi,kjz≥m∗,m∗+rm∗−pi,kjz;ifpi,kjz−1≤m∗≤pikjzorpi,kjz−1≥m∗≥pi,kjz,m∗+2r−1m∗−pi,kjt;ifm∗≤pi,kjz−1≤pi,kjzorm∗≥pi,kjz−1≥pi,kjz,m∗+2r−1m∗−piklz−1.(16)pi,kjz+1=ifpi,kjz−1≤pi,kjz≤m∗orpi,kjz−1≥pi,kjz≥m∗,m∗+2r−1m∗−pi,kjz;ifpi,kjz−1≤m∗≤pi,kjzorpi,kjz−1≥m∗≥pi,kjz,pi,kjz−1+rpi,kjz−pi,kjz−1;ifm∗≤pi,kiz−1≤pi,kjzorm∗≥pi,kjz−1≥pi,kjz,m∗+2r−1m∗−pi,kjz−1.To balance exploitation, party switch is adopted [20]. Adaptive variable λ is applied; that is drastically minimized from 1 to 0 in the entire iteration method. All the candidates are chosen as per the possibility λ as well as substituted with the worst member of the arbitrarily chosen party.
(17)q=argmaxi≤j≤nfpij.During the election stage, the winner in the constituency is gained as follows:(18)q=argmini≤j≤nfpij,(19)cj∗=pqj.Figure2 shows how PO updates the location of CFNNs. The position of the global optimum is shown by the star symbol. With regard to the party leader, dotted arrows show the position updating of party 3 members, and plain arrows show the position updating of party 3 members in relation to the constituency winners.Figure 2
Depiction of the position updating of CFNNs through PO.


## 3. Performance Validation

This section investigates the intrusion detection outcomes of the PO-CFNN model on three distinct datasets with 80 : 20 for training and testing. Table1 and Figures 3 and 4 report the comparative results of the PO-CFNN model with existing methods on the test NSL-KDD++ dataset [21]. The results indicated that the DBN model has gained ineffective outcomes over the other methods with an accuracy of 95.44%. At the same time, the deep NN model has resulted in a slightly increased accuracy of 96.55%. In line with this, the ASCH and RNN models have obtained moderately improved accuracy of 98.74% and 97.70%, respectively. Though the SHOLEN model has accomplished reasonable accuracy of 99.62%, the PO-CFNN model has showcased superior results with an accuracy of 99.86%.Table 1
Comparative analysis of PO-CFNN technique with recent approaches to the NSL-KDD++ dataset.
MethodsAccuracyPrecisionRecallF scorePO-CFNN99.8699.8999.5899.72SHOLEN model [22]99.6299.7699.4999.60ASCH model [23]98.7496.6298.8293.36RNN model97.7096.2595.5593.36Deep NN model96.5595.5895.4792.50DBN model [24]95.4494.2593.2892.39Figure 3
Precn and recal analysis of PO-CFNN technique under NSL-KDD++ dataset.Figure 4
Accy and Fscore analysis of PO-CFNN technique under NSL-KDD++ dataset.Figure5 establishes the ROC analysis of the PO-CFNN technique on the NSL-KDD++ dataset. The figure exposed that the PO-CFNN technique has reached an enhanced outcome with a higher ROC of 99.9928.Figure 5
ROC analysis of PO-CFNN technique under NSL-KDD++ dataset.Table2 and Figures 6 and 7 report the comparative results of the PO-CFNN model with existing methods on the test UNSWNB15 dataset [25]. The results indicated that the DBN model has gained ineffective outcomes over the other methods with an accuracy of 98.92%. Simultaneously, the deep NN model has resulted in a slightly increased accuracy of 98.57%. Also, the ASCH and RNN models have obtained moderately improved accuracy of 98.88% and 98.99%, respectively. But, the SHOLEN model has accomplished reasonable accuracy of 98.99%, and the PO-CFNN model has showcased superior results with an accuracy of 99.46%.Table 2
Comparative analysis of PO-CFNN technique with recent approaches to UNSWNB15 dataset.
MethodsAccuracyPrecisionRecallF scorePO-CFNN99.4699.7599.6299.76SHOLEN model98.9999.6399.4599.68ASCH model98.8896.6898.7493.58RNN model98.9996.4097.9494.55Deep NN model98.5795.6095.6492.98DBN model98.9294.2893.3093.61Figure 6
Precn and recal analysis of PO-CFNN technique under UNSWNB15 dataset.Figure 7
Accy and Fscore analysis of PO-CFNN technique under UNSWNB15 dataset.Figure8 depicts the ROC analysis of the PO-CFNN technique on the UNSWNB15 dataset [26]. The figure exposed that the PO-CFNN technique has reached an enhanced outcome with a higher ROC of 99.9378.Figure 8
ROC analysis of PO-CFNN technique under UNSWNB15 dataset.Table3 and Figures 9 and 10 report the comparative results of the PO-CFNN technique with existing methods on the test CIDCC-2017 dataset. The results indicated that the DBN model has gained ineffective outcomes over the other methods with an accuracy of 98.83%. Likewise, the deep NN model has resulted in a slightly increased accuracy of 98.69%. The ASCH and RNN models have obtained moderately improved accuracy of 98.65% and 98.92%, respectively. Finally, the SHOLEN model has accomplished reasonable accuracy of 99.05%; the PO-CFNN model has showcased superior results with an accuracy of 99.38%.Table 3
Comparative analysis of PO-CFNN technique with recent approaches on the CIDCC-2017 dataset.
MethodsAccuracyPrecisionRecallF scorePO-CFNN99.3899.6999.6699.69SHOLEN model99.0599.6199.5299.54ASCH model98.6595.5598.7493.31RNN model98.9295.9695.4694.21Deep NN model98.6993.3192.1090.93DBN model98.8395.8794.9792.37Figure 9
Precn and recal analysis of PO-CFNN technique under CIDCC-2017 dataset.Figure 10
Accy and Fscore analysis of PO-CFNN technique under the CIDCC-2017 dataset.Figure11 portrays the ROC analysis of the PO-CFNN technique on the CIDCC-2017 dataset. The figure exposed that the PO-CFNN technique has reached an enhanced outcome with a higher ROC of 99.9900. Figure 12 provides a comparative study for all datasets as an average accuracy for all models. The figure shows the superiority of the proposed PO-CFNN model.Figure 11
ROC analysis of PO-CFNN technique under the CIDCC-2017 dataset.Figure 12
Average accuracy for all models with all datasets.


## 4. Conclusion

In this work, a novel PO-CFNN approach for identifying and classifying IoT intrusions has been created. Before classification and parameter optimization, the PO-CFNN algorithm undergoes preprocessing and preclassification. In the beginning, the network data is preprocessed to make it easier to analyze. The CFNN algorithm is used to identify and classify intrusions in the IoT environment. Final adjustments to the CFNN model’s parameters are made using the PO algorithm at this point. When compared to other contemporary techniques, the PO-CFNN algorithm performed better in an experiment on a benchmark dataset. As a result, the PO-CFNN approach offers superior performance over the alternatives. In future work, feature selection techniques might be developed utilizing the metaheuristics algorithms.



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