Comparative Performance Evaluation of Intrusion Detection Methods for In-Vehicle Networks

Ji, Haojie; Wang, Yunpeng; Qin, Hongmao; Wang, Yongjian; Li, Honggang

doi:10.1109/access.2018.2848106

Cited by 41 publications

(16 citation statements)

References 26 publications

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“…Given the vulnerability of CAN bus with respect to lack of encryption and authentication, many researchers have proposed different anomaly detection techniques for detecting injection attacks. A comprehensive survey of anomaly detection can be found in [1], [23], [24]. This includes rule/frequency-based techniques, sequence-based and machine learning-based techniques.…”

Section: Related Workmentioning

confidence: 99%

Detecting Low-Rate Replay-Based Injection Attacks on In-Vehicle Networks

et al. 2020

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The lack of security in today's in-vehicle network make connected vehicles vulnerable to many types of cyber-attacks. Replay-based injection attacks are one of the easiest type of denial-of-service attacks where the attacker floods the in-vehicle network with malicious traffic with intent to alter the vehicle's normal behavior. The attacker may exploit this vulnerability to launch targeted low-rate injection attacks which are difficult to detect because the network traffic during attacks looks like regular network traffic. In this paper, we propose a sequence mining approach to detect low-rate injection attacks in Control Area Network (CAN). We discuss four different types of replay attacks that can be used by the adversary, and evaluate the effectiveness of proposed method for varying attack characteristics and computational performance for each of the attacks. We observe that the proposed sequence-based anomaly detection achieves over 99% f-score, and outperforms existing dictionary based and multi-variate Markov chain based approach. Given that the proposed technique only uses CAN identifiers, the techniques could be adaptable to any type of vehicle manufacturer.

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Section: Related Workmentioning

confidence: 99%

Detecting Low-Rate Replay-Based Injection Attacks on In-Vehicle Networks

et al. 2020

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“…5, very few works have compared their proposed solutions with others that have similar conditions. It is necessary to evaluate and validate the significant differences of the proposed method among different methods so that the concerned method can achieve optimal performance [87].…”

Section: Discussion and Summarymentioning

confidence: 99%

Intrusion detection system for automotive Controller Area Network (CAN) bus system: a review

Lokman

Othman

Bakar

2019

J Wireless Com Network

165

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The modern vehicles nowadays are managed by networked controllers. Most of the networks were designed with little concern about security which has recently motivated researchers to demonstrate various kinds of attacks against the system. In this paper, we discussed the vulnerabilities of the Controller Area Network (CAN) within invehicle communication protocol along with some potential attacks that could be exploited against it. Besides, we present some of the security solutions proposed in the current state of research in order to overcome the attacks. However, the main goal of this paper is to highlight a holistic approach known as intrusion detection system (IDS) which has been a significant tool in securing networks and information systems over the past decades. To the best of our knowledge, there is no recorded literature on a comprehensive overview of IDS implementation specifically in the CAN bus network system. Thus, we proposed an in-depth investigation of IDS found in the literature based on the following aspects: detection approaches, deployment strategies, attacking techniques, and finally technical challenges. In addition, we also categorized the anomaly-based IDS according to these methods, e.g., frequencybased, machine learning-based, statistical-based, and hybrid-based as part of our contributions. Correspondingly, this study will help to accelerate other researchers to pursue IDS research in the CAN bus system.

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“…This article helps to understand the advantages of test methods in the detection performance of in-vehicle networks. Furthermore, it promotes the application of detection technologies to safety issues in the automotive industry [24]. Zhang et al took intrusion detection system (IDS) as the research object, established an IDS model based on data mining, obtained experimental results, and drew relevant experimental conclusions.…”

Section: Literature Reviewmentioning

confidence: 99%

An Exhaustive Research on the Application of Intrusion Detection Technology in Computer Network Security in Sensor Networks

Wang

Sharma

et al. 2021

Journal of Sensors

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Intrusion detection is crucial in computer network security issues; therefore, this work is aimed at maximizing network security protection and its improvement by proposing various preventive techniques. Outlier detection and semisupervised clustering algorithms based on shared nearest neighbors are proposed in this work to address intrusion detection by converting it into a problem of mining outliers using the network behavior dataset. The algorithm uses shared nearest neighbors as similarity, judges whether it is an outlier according to the number of nearest neighbors of a data point, and performs semisupervised clustering on the dataset where outliers are deleted. In the process of semisupervised clustering, vast prior knowledge is added, and the dataset is clustered according to the principle of graph segmentation. The novelty of the proposed algorithm lies in outlier detection while effectively avoiding the dependence on parameters, thus eliminating the influence of outliers on clustering. This article uses real datasets: lypmphography and glass for simulation purposes. The simulation results show that the algorithm proposed in this paper can effectively detect outliers and has a good clustering effect. Furthermore, the experimentation reveals that the outlier detection-based SCA-SNN algorithm has the best practical effect on the dataset without outliers, clearly validating the clustering performance of the outlier detection-based SCA-SNN algorithm. Furthermore, compared to the other state-of-the-art anomaly detection method, it was revealed that the anomaly detection technology based on outlier mining does not require a training process. Thus, they overcome the current anomaly detection problems caused due to incomplete normal patterns in training samples.

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Comparative Performance Evaluation of Intrusion Detection Methods for In-Vehicle Networks

Cited by 41 publications

References 26 publications

Detecting Low-Rate Replay-Based Injection Attacks on In-Vehicle Networks

Detecting Low-Rate Replay-Based Injection Attacks on In-Vehicle Networks

Intrusion detection system for automotive Controller Area Network (CAN) bus system: a review

An Exhaustive Research on the Application of Intrusion Detection Technology in Computer Network Security in Sensor Networks

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