Integrating Adversary Models and Intrusion Detection Systems for In-vehicle Networks in CANoe

Jichici, Camil; Groza, Bogdan; Murvay, Pal-Stefan

doi:10.1007/978-3-030-41025-4_16

Cited by 6 publications

(7 citation statements)

References 17 publications

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“…However, there was no defense mechanism proposed against the attacks. Unlike our study, which offers mitigation measures, the proposed testbed in [31] facilitates the investigation of the impact of adversarial examples on IDS; the software offers no room for testing mitigation measures. Although the work of [38] is on CAV, the study focus only considered vehicular ad-hoc networks using synthetic datasets and binary classification problems.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, Yang et al [23] demonstrate the vulnerability of deep neural networks for network intrusion detection system (NIDS) to adversarial examples using model substitution and black-box-based zeroth-order optimisation (ZOO) and generative adversarial network (GAN) attacks. In another study, a testbed consisting of an adversarial model embedded in the IDS was designed to facilitate security evaluation of CAN system design [31]. However, the developed tool provides no function for testing mitigation measures.…”

Section: Background a Related Workmentioning

confidence: 99%

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Statistical Detection of Adversarial Examples in Blockchain-Based Federated Forest In-Vehicle Network Intrusion Detection Systems

et al. 2022

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The internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in-vehicle network from cyber-attacks. Blockchainbased Federated Forests (BFFs) could be used to train ML models based on data from IoV entities while protecting the confidentiality of the data and reducing the risks of tampering with the data. However, ML models are still vulnerable to evasion, poisoning and exploratory attacks by adversarial examples. The BFF-IDS offers partial defence against poisoning but has no measure for evasion attacks, the most common attack/threat faced by ML models. Besides, the impact of adversarial examples transferability in CAN IDS has largely remained untested. This paper investigates the impact of various possible adversarial examples on the BFF-IDS. We also investigated the statistical adversarial detector's effectiveness and resilience in detecting the attacks and subsequent countermeasures by augmenting the model with detected samples. Our investigation results established that BFF-IDS is very vulnerable to adversarial examples attacks. The statistical adversarial detector and the subsequent BFF-IDS augmentation (BFF-IDS(AUG)) provide an effective mechanism against the adversarial examples. Consequently, integrating the statistical adversarial detector and the subsequent BFF-IDS augmentation with the detected adversarial samples provides a sustainable security framework against adversarial examples and other unknown attacks. INDEX TERMSAdversarial examples, artificial intelligent (AI), blockchain, controller area network (CAN), federated learning, intrusion detection system (IDS).

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Section: Discussionmentioning

confidence: 99%

Section: Background a Related Workmentioning

confidence: 99%

Statistical Detection of Adversarial Examples in Blockchain-Based Federated Forest In-Vehicle Network Intrusion Detection Systems

et al. 2022

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“…However, there was no defense mechanism proposed against the attacks. Unlike our study, which offers mitigation measures, the proposed testbed in [27] facilitates the investigation of the impact of adversarial examples on IDS; the software offers no room for testing mitigation measures. Although the work of [28] is on CAV, the study focus only considered vehicular ad-hoc networks using synthetic datasets and binary classification problems.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, Yang et al [23] demonstrate the vulnerability of deep neural networks for NIDS to adversarial examples using model substitution and black-box-based zeroth-order optimization (ZOO) and generative adversarial network (GAN) attacks. In another study, a testbed consisting of an adversarial model embedded in the IDS was designed to facilitate security evaluation of CAN system design [27]. However, the developed tool provides no function for testing mitigation measures.…”

Section: Related Workmentioning

confidence: 99%

Statistical Detection of Adversarial Examples in Blockchain-Based Federated Forest In-Vehicle Network Intrusion Detection System

Aliyu¹,

Engelenburg²,

Mu’azu³

et al. 2022

SSRN Journal

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“…This working scenario has also been considered by other works, e.g. [44]. For this, we configured inside the environment a CAN node to replay the genuine dataset and another node to inject malicious frames.…”

Section: A Setup For Off-line Analysismentioning

confidence: 99%

Android Head Units vs. In-Vehicle ECUs: Performance Assessment for Deploying In-Vehicle Intrusion Detection Systems for the CAN Bus

et al. 2022

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Following the numerous attacks that exploited vulnerabilities of Controller Area Networks (CAN), intrusion detection systems have become a topic of prime importance for in-vehicle buses. Newer in-vehicle communication layers, such as CAN-FD, despite the larger payloads which can easily integrate cryptographic elements, need similar attention. But detecting intrusions may call for demanding algorithms that are not computationally cheap while timely detection is necessary in order to process frames in realtime and take the appropriate actions. In this work we evaluate the performance of several binary classifiers on traditional in-vehicle Electronic Control Units (ECUs) and compare them to modern Android devices which have become widespread inside cars with the adoption of Android-capable infotainment systems. Needless to say, these modern devices benefit from higher computational and memory resources while cloud connectivity may alleviate computational costs even further. Contrasting between traditional controllers and Android devices has become necessary and so far there have been little efforts in this direction. To create a realistic testbed, we use collected in-vehicle CAN bus traffic from an SUV as well as more demanding logs from Advanced Driver-assistance Systems (ADAS) implemented on CAN-FD which we augment with adversarial activity.

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Integrating Adversary Models and Intrusion Detection Systems for In-vehicle Networks in CANoe

Cited by 6 publications

References 17 publications

Statistical Detection of Adversarial Examples in Blockchain-Based Federated Forest In-Vehicle Network Intrusion Detection Systems

Statistical Detection of Adversarial Examples in Blockchain-Based Federated Forest In-Vehicle Network Intrusion Detection Systems

Statistical Detection of Adversarial Examples in Blockchain-Based Federated Forest In-Vehicle Network Intrusion Detection System

Android Head Units vs. In-Vehicle ECUs: Performance Assessment for Deploying In-Vehicle Intrusion Detection Systems for the CAN Bus

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