# Energy-Aware Intrusion Detection Model for Internet of Vehicles Using Machine Learning Methods

Authors: Lu Lihua
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

With increasing development of Internet of Things (IoT) technology, wireless communications, big data, and smart applications, vehicular communications have become ubiquitous in smart cities, smart transportation systems, and Internet of Vehicles (IoV) environments. In this paper, a new Energy-aware Intrusion Detection System (EIDS) based on intelligent two-phase contract management model is presented for vehicle-to-vehicle (V2V) strategy in the IoV environments. In this strategy, the proposed EIDS predicts safe and energy-efficient end-to-end points for communication between existing vehicles in the IoV. The contract management process shows how the vehicles are connected together with a safe condition to transfer information. For prediction phase, a regression algorithm is applied to evaluate the proposed EIDS according to NSLKDD data set in the IoV environments. Simulation experiments show that the proposed regression-based EIDS strategy can effectively improve the accuracy and precision factors with 90% and 84%, respectively, and greatly minimize execution time by 4 seconds with respect to other machine learning algorithms.


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## Body

## 1. Introduction

Today, vehicular communications have developed emerging topics on intelligent transportation systems with respect to wireless distribution and smart devices in the Internet of Things (IoT) environments [1, 2]. In the IoT, smart devices perform communication at different locations by providing a level of transparency among users and maintained an interconnected smart network. According to the main concept of the IoT [3], vehicular communications have wide collaboration between smart devices and big data on new intelligent concept of Internet of Vehicles (IoV) [4–6]. In the IoV environments, the communication model is divided into three statuses including vehicle to vehicle (V2V) [7, 8], vehicle to infrastructure (V2I) [9], and vehicle to people (V2P) [8, 10]. In these models, data transmission as an important problem statement has usually applied smart sensors and intelligent applications with minimum energy consumption to exchange and transfer the information between safety of vehicles [11], applications, devices, sensors, and peoples [12]. In this problem statement, Intrusion Detection Systems (IDS) have critical and emerging issues for supporting safety [13, 14], security [15], and privacy of data transmission [16] and information retrieval of intelligent transportation systems in the IoV [17]. The data transmission accumulated by V2V, V2I, and V2P case studies [8, 18, 19] can be managed securely to provide various cloud-edge services such as road safety, smart parking reservations, traffic management, vehicular routing management, and emergency issues [20].According to the above critical problem statements on the IoV environments, this paper presents a new Energy-aware Intrusion Detection System (EIDS) to provide safety conditions for data transmission between vehicles as a V2V case study to avoid the existing attacks and critical points. In this paper, a machine learning method is presented to predict optimal energy consumption between vehicles to transfer data with safe and secured infrastructure in the IoV environments. The main contributions of this research are shown as follows:(i)
Proposing an energy-aware intrusion detection model for managing a safe data transmission method for V2V scenarios in IoV(ii)
Applying regression algorithm as the machine learning method to predict optimal secured infrastructure in the IoV environments(iii)
Increasing accuracy and precision factors for predicting existing attacks and critical points in the EIDSThe organization of the paper is presented as follows: Section2 illustrates a comprehensive literature review for security-aware and energy consumption models in the IoT and intelligent transportation systems. Section 3 shows a conceptual model of the proposed energy-aware intrusion detection model based on regression algorithm. Section 4 presents simulation parameters and experimental results based on comparison of existing machine learning algorithms and discussion on evaluation factors, respectively. Finally, Section 5 provides a brief discussion on the experimental results, conclusion, and future works.


## 2. Related Work

In this section, some new relevant case studies are discussed and analyzed as a literature review for intrusion detection strategies using machine learning and evolutionary algorithms in the IoV, IoT, and vehicular ad hoc network (VANET). Many research studies have evaluated security-based cloud-edge service scheduling and allocation for energy-aware IoV systems [21–24].Subba et al. [25] provided a theory and algorithm using the IDS framework for VANET security. VANETs have sensors and On-Board Units (OBUs). OBUs use Road Side Units (RSUs) and IEEE 802.11p for connecting vehicles. VANETs are susceptible to diverse kinds of network attacks so they used Intrusion Detection Systems (IDSs) to solve them. Some IDS properties are not suitable for VANET such as IDS traffic volume, bandwidth limitation, dynamic network topology, communication overhead, and scalability which need to be fixed. They presented a new clustering algorithm that produced constant vehicular clusters using vehicular data. The proposed Cluster Head (CH) selection algorithm is based on the Vickrey-Clarke-Groves method. They suggested a game theory-based multilayered intrusion detection framework for VANET to detect different kinds of attacks in VANET. They used NS3 and Simulation of Urban Mobility (SUMO) for simulation. Simulation of the interaction between the IDS and the malicious vehicle reduces the size of IDS traffic by embracing a probabilistic IDS showing strategy based on the Nash equilibrium of the game. The problem is that the proposed algorithm is for vehicles that stop or move slowly. Their future work is to present a dynamic clustering algorithm and also improve components and develop the project.Kang et al. [26] and his team have proposed an extremely impressive accidental confirmation protocol that contains homomorphic encryption to permit any personal vehicle to self-produce every number of confirmed personalities to get complete obscurity in VANETs. The suggested protocol barricades vehicles by detecting every prohibited person and increasing traceability. The purpose of this paper has been to propose an extremely effective accidental confirmation order in VANETs, the name of RAU+. The results of this paper show that their suggested RAU+ protocol is rather effective than another protocol and can efficiently decrease the network overhead. The disadvantage of this paper is that it requires more testing in the future to improve their protocol which can be spread to IoT schedules.In [27], an authentication scheme has been proposed that has led to a complete summary of VANET, and it has been seen that this scheme meets VANET security requirements via security analysis. The signal phase is divided into two stages, and the previous calculation method is used to reduce the calculation cost in the signal stage. Road Side Unit (RSU) has been able to collect multiple signatures in a single unit, and the total length of the signature is a fixed size, which significantly reduces the transmission between the RSU and the application server and improves the verification efficiency for the application. In the next work, to reduce the cost of calculations and communications, the use of a lighter signature plan is considered.Chen et al. [28] and his colleagues presented a new model based on barriers, and link performance on the highway is presented using the obstacle-based channel model. In this research, the authors have used Markov chain realistic channel model and dual-slop path loss model to evaluate this. Assessment measures can be referred to reliability and time. Evaluation and model of empirical results indicate that this system has effectiveness through security analysis and is also used to evaluate the end-end performance. The advantages of this project are using the real channel simulator, and its drawbacks can be pointed to the long-term connection time.Zhang et al. [29] suggested a new design for the dissemination of safety messages for quality-based urban IoV provided for accurate estimation of connection probability among vehicles. In this model, the CFs algorithm is used. Simulation results represent a good approximation of the model and the superiority of this protocol. To evaluate the research, time and probability factors have been used. The advantages of this paper are the superiority of it is performing compared to similar studies, and its disadvantages can be referred to as delay.In [30], the Media Access Control (MAC) program is deliberately based on multilateral cooperation to eliminate delays and data interference in the automotive network. The protocol transmits security data with the corresponding sensors installed along the road and sends the DA search packet in the SCHI gap to obtain RSU-covered car data. When transferring this data, the vehicle node, which has a security message for sending or receiving CCH gap subscribers and transferring nonaccident data, is obtained using the multifaceted reservation mechanism in the SCHI gap. With RSU coordination, security information is tracked by each node in order. To achieve the VANET terminal, RSU has played the role of wireless energy absorption, which has led to uninterrupted channel transmissions, reduced channel delays, and improved channel efficiency.Yaqub et al. [31] provided a method named cooperative video retrieval scheme (CoRe). Streaming media has become a significant factor to satisfy VANET users, while downloading videos with proper qualities causes bandwidth consumption and may leave insufficient bandwidth for quality of service. To avoid such issues, the authors suggested a communicative system in which vehicles can request more bandwidth in case of sharing. They can ask another vehicle for bandwidth sharing, downloading a part of media, or forwarding the demanded video via a link. The system actually chooses a nearby neighbor vehicle to request, in order to preserve the V2V connection, and it must have sufficient bandwidth to share. Based on the results, CoRe helped users to obtain a better quality of the video. They decided to involve 5G and ICN technologies with a CoRe system in the future.


## 3. Proposed Method

In this section, a new energy-based vehicle-to-vehicle collaboration is presented. Then, a new IDS approach is applied for the proposed V2V strategy to check and analyze performance of the IoV environments using machine learning methods. In the IoV environments, vehicles collaborate together in a secured end-to-end capacity using IoT application smart devices, sensors, and interconnection methods. On existing interconnection and intraconnection methods, the security is a significant challenge for a safe condition on data transmission between vehicles in the IoV environments. On the other hand, energy consumption of IoT nodes is a critical issue that represents the performance evaluation of vehicular communications in IoV applications. Certainly, in this section, we present a new energy-aware intrusion detection method with respect to minimizing energy consumption of vehicles as a fundamental parameter in the IoV environments.In the IoV environment, some important factors such as traffic road scheduling, the moving speed of vehicles [32], the density of vehicles movements, and the infrastructure of the network change dynamically. Based on the abovementioned factors, each data transmission strategy between two or more than three vehicles should be examined with energy consumption, delay, and response time metrics. According to the V2V strategy, each vehicle has a communication range for collaboration with other vehicles in the IoV environment. For creating a data transmission connection, energy consumption between two vehicles should be examined before the intraconnection protocol [33]. According to Equation (1), the earned energy factor for transmitting information between two vehicles is evaluated as follows [34, 35]:
(1)EVij=ETi×NO+ETj×NI,where ET is the energy consumption for transmitting data in vehicles i and j, NO is the number of transmission packets that is sent for each communication round, and NI is the number of transmission packets that is received in each communication round [36, 37].Also, the energy consumed in the data transmission between a vehicle and wireless server is calculated as follows according to the following equation [38]:
(2)EW=ETi×M+ESj×K,where M is the number of sent or received packets for each communication round to the wireless server, ESj is the energy consumption for transmitting data to the vehicle j, and K is the number of sent packets for each communication round.The total energy consumption metric for one communication round between existing vehicles and servers is computed according to the following equation [39]:
(3)ET=∑EVij×NV+∑EW×NS,where NV is the number of existing vehicles and NS is the number of existing wireless servers in a communication round.To create a data transmission round, each vehicle as an active node in the IoV sends a request to the neighbors with a broadcast mode. Each other vehicle receives existing request and checks with information for all available candidates to transfer data between activated nodes. According to Figure1, if a communication distance is higher than the communication range or there is no an activated vehicle in circle of requested vehicle, then targeted node sends request to the wireless server. After finding an appropriate and alive transmission link between existing vehicles and servers according to the V2V strategy [40], link establishment is considered to finalize data transmission link. Then, energy consumption of the established link is examined to check minimum energy consumption between all nodes including activated vehicles and wireless servers. According to Equation (1), the produced energy factor for transmitting information between two vehicles is evaluated. Also, based on Equation (2) [41], the energy consumed in the data transmission between the vehicle and wireless server is calculated. Finally, Equation (3) calculates the total energy consumption metric for one communication round between existing vehicles and the IoV servers. If there is an optimized energy-efficient established link for data transmission, then the intrusion detection method is applied. Otherwise, system reassigns finding transmission link between available vehicles and servers. When an optimized energy-efficient link is selected, the proposed intrusion detection method (EIDS) is activated [42, 43]. In this step, the regression algorithm as the proposed machine learning method selects applied data set for training process for detecting the malicious behaviors and attacks. The existing data set is divided into two sides; the first content is applied for train process as %70, and the other content %30 is evaluated for test process. In the train process, if intrusion detection has safe result for a transmitted link, then real data transmission and packets are transferred in this link. Otherwise, the selected link is unsafe for communication.Figure 1
The EIDS flowchart based on regression prediction algorithm in IoV.According to the above mentioned framework, the proposed regression-based EIDS method checks minimum energy consumption for each selected transmission link and then proceeds intrusion detection process for existing data set.


## 4. Experimental and Simulation Results

This section proposes performance evaluation and comparison for the proposed EIDS with regression algorithm and other algorithms including the Support Vector Machine (SVM) [44], Random Forest (RF) algorithm [45], Multilayer Perceptron (MLP) algorithm [46], and Decision Tree (DT) algorithm [47]. We have applied the existing algorithms on the NSLKDD data set [48] as our case study (https://www.unb.ca/cic/datasets/nsl.html) with existing information. Also, WEKA toolkit [49] as simulation environment was used to evaluate the proposed EIDS case study using a system with Windows 10, Intel i5 3.10 GHz, 8 GB RAM. Also, a brief illustration about the applied NSLKDD data set is shown in Table 1 [50]. Then, the performance metrics used in the evaluation are introduced in Table 2.Table 1
Data set parameters.
DescriptionTrain setTest setAttacks3,925,650250,436Normal972,78160,591Table 2
Simulation parameters.
DescriptionValues (default)Number of vehicles10Communication range between each vehicle100, 200, 300, 400, 500Environment dimension50∗50∗50Transmission rate (Mbps)100According to the proposed Energy-aware IDS approach, we have presented a new regression-based prediction approach to detect minimum energy consumption for a data transmission procedure. For evaluating this procedure, some important prediction factors such as accuracy, precision, and execution time have been analyzed based on existing machine learning algorithms [51]. The experimental results evaluate important prediction factors including accuracy, precision, and execution time on the results of existing machine learning algorithms [52]. Table 3 shows prediction factors with respect to the following training parameters: True Positive (TP), True Negative (TN), False Positive (FP) [53], and False Negative (FN) metrics [54].Table 3
Prediction factors.
Prediction factorDescriptionAccuracyTP+TNTP+TN+FP+FNPrecisionTPTP+FP


### 4.1. Simulation Results

We have simulated the proposed regression-based EIDS case study with respect to other algorithms in MATLAB environment that simulates the proposed prediction method for V2V strategy in the IoV environment. To evaluate the performance of the proposed algorithm, we considered five communication ranges between each vehicle for V2V strategy as 100, 200, 300, 400, and 500 in the IoV environment.Figure2 represents evaluation of accuracy factor with respect to each communication range step. According to the observed diagram, it can be achieved that the proposed regression-based EIDS has optimal score for the accuracy of prediction. The results obtained from the proposed regression-based EIDS method in the IoV were compared with other machine learning algorithms. This comparison on accuracy evaluation shows that the proposed regression-based EIDS has achieved a maximum accuracy factor of 90% for 10 vehicles. Moreover, another optimized machine leering algorithm is the RF algorithm that has approximately 78% accuracy higher than SVM, MLP, and DT prediction algorithms.Figure 2
Evaluation of accuracy factor for energy-aware intrusion detection strategy in IoV.According to Figure3, the proposed regression-based EIDS method has achieved higher precision metric for existing communication ranges between 80% and 85%. But the RF method has achieved only 74% just for communication range 200 with high precision, the SVM algorithm has shown 74% just for communication range 300, and the MLP algorithm has performed 71% of precision just for communication range 300 as best results for each prediction method. This evaluation illustrates that the DT algorithm has only a small amount of precision lower than 55% for detecting attacks in the IoV scenarios. With respect to this comparison, we conclude that the precision factor has different evaluation results with different communication ranges in other algorithms. However, the proposed regression-based EIDS method has attained maximum precision for the overall communication ranges.Figure 3
Evaluation of precision metric for energy-aware intrusion detection strategy in IoV.Finally, to assess execution time of this prediction, we can observe that the proposed regression-based EIDS has gained 4.5 s for communication range 100, 6.2 s for communication range 200, 14.8 s for communication range 300, 18.9 s for communication range 400, and 20.2 s of execution time for communication range 500 according to Figure4. So, the proposed method significantly has minimum execution time for the overall communication ranges. On the other hand, the execution time of DT algorithm is 69.8 s for communication ranges 300 and 500.Figure 4
Execution time evaluation for the proposed EIDS strategy in the IoV environment.


## 4.1. Simulation Results

We have simulated the proposed regression-based EIDS case study with respect to other algorithms in MATLAB environment that simulates the proposed prediction method for V2V strategy in the IoV environment. To evaluate the performance of the proposed algorithm, we considered five communication ranges between each vehicle for V2V strategy as 100, 200, 300, 400, and 500 in the IoV environment.Figure2 represents evaluation of accuracy factor with respect to each communication range step. According to the observed diagram, it can be achieved that the proposed regression-based EIDS has optimal score for the accuracy of prediction. The results obtained from the proposed regression-based EIDS method in the IoV were compared with other machine learning algorithms. This comparison on accuracy evaluation shows that the proposed regression-based EIDS has achieved a maximum accuracy factor of 90% for 10 vehicles. Moreover, another optimized machine leering algorithm is the RF algorithm that has approximately 78% accuracy higher than SVM, MLP, and DT prediction algorithms.Figure 2
Evaluation of accuracy factor for energy-aware intrusion detection strategy in IoV.According to Figure3, the proposed regression-based EIDS method has achieved higher precision metric for existing communication ranges between 80% and 85%. But the RF method has achieved only 74% just for communication range 200 with high precision, the SVM algorithm has shown 74% just for communication range 300, and the MLP algorithm has performed 71% of precision just for communication range 300 as best results for each prediction method. This evaluation illustrates that the DT algorithm has only a small amount of precision lower than 55% for detecting attacks in the IoV scenarios. With respect to this comparison, we conclude that the precision factor has different evaluation results with different communication ranges in other algorithms. However, the proposed regression-based EIDS method has attained maximum precision for the overall communication ranges.Figure 3
Evaluation of precision metric for energy-aware intrusion detection strategy in IoV.Finally, to assess execution time of this prediction, we can observe that the proposed regression-based EIDS has gained 4.5 s for communication range 100, 6.2 s for communication range 200, 14.8 s for communication range 300, 18.9 s for communication range 400, and 20.2 s of execution time for communication range 500 according to Figure4. So, the proposed method significantly has minimum execution time for the overall communication ranges. On the other hand, the execution time of DT algorithm is 69.8 s for communication ranges 300 and 500.Figure 4
Execution time evaluation for the proposed EIDS strategy in the IoV environment.


## 5. Conclusion

In this paper, a new Energy-aware Intrusion Detection System (EIDS) was presented for managing safe transitions and information in the IoV environments. Since the EIDS is based on prediction approach, machine learning techniques were applied to enhance quality of prediction for malicious and existing attacks according to train and test data sets with respect to low energy consumption for each vehicle. The experimental results have shown the efficiency performance of the proposed machine learning algorithms with minimum energy consumption, high accuracy, and maximum precision in the IoV environments. Also, the prediction time of the EIDS with the regression algorithm is lower than that of the other machine learning algorithms. Finally, the proposed algorithm guaranteed safe data transactions between the maximum numbers of vehicles in the IoV environments. In the future work, a new feature selection method for the effective predictable model in machine learning approach can be presented to optimize efficient accuracy and energy consumption with a high-quality priority in the vehicular communications.



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