“My autonomous car is an elephant”: A Machine Learning based Detector for Implausible Dimension

Monteuuis, Jean-Philippe; Petit, Johann; Berri, Sara; Labiod, Houda; Mafrica, Stefano; Servel, Alain

doi:10.1109/ssic.2018.8556651

Cited by 13 publications

(4 citation statements)

References 11 publications

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“…Using this partition we can calculate, similarly to other studies [7] [16], the following performance metrics: Intuitively, the Recall characterizes the system ability to flag all the misbehaving messages, Whereas the P recision characterizes the system ability to not consider misbehaving as genuine messages. The F 1 score is the harmonic mean of Recall and P recision.…”

Section: ) Considered Attacksmentioning

confidence: 99%

CaTch: A Confidence Range Tolerant Misbehavior Detection Approach

Kamel

Kaiser

Jemaa

et al. 2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

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Misbehavior detection is a challenging problem that needs to be addressed in vehicular communications. Misbehavior detection consists of monitoring the semantic of the exchanged messages to identify potential misbehaving entities. This is achieved by performing plausibility and consistency checks on exchanged beacon and warning messages. However, existing misbehavior detection solutions ignore the mandatory information on data inaccuracy, being gathered by the vehicular sensors. In this paper, we propose CaTch, an embedded misbehavior detection solution that integrates data inaccuracy when performing plausibility and consistency checks. Through extensive simulations, we show that CaTch is able to attribute an accurate uncertainty factor to misbehaving nodes and that it performs better than the state-of-the-art solutions.

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Section: ) Considered Attacksmentioning

confidence: 99%

CaTch: A Confidence Range Tolerant Misbehavior Detection Approach

Kamel

Kaiser

Jemaa

et al. 2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

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“…Notice the inverse relationship of EXP with τ , which is given by P [EXP] = 1 − τ . As such, referring to (12), one can infer that the reward is piled faster with a larger τ : i.e., r t,i ∝ τ . This relationship forms the chain between the result of a packet transmission linked to the vehicle's learning in the next round.…”

Section: B Results and Discussionmentioning

confidence: 99%

Reinforcement Learning for Accident Risk-Adaptive V2X Networking

Kim

2020

Preprint

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The significance of vehicle-to-everything (V2X) communications has been ever increased as connected and autonomous vehicles (CAVs) get more emergent in practice. The key challenge is the dynamicity: each vehicle needs to recognize the frequent changes of the surroundings and apply them to its networking behavior. This is the point where the need for machine learning is raised. However, the learning itself is extremely complicated due to the dynamicity as well, which necessitates that the learning framework itself must be resilient and flexible according to the environment. As such, this paper proposes a V2X networking framework integrating reinforcement learning (RL) into scheduling of multiple access. Specifically, the learning mechanism is formulated as a multi-armed bandit (MAB) problem, which enables a vehicle, without any assistance from external infrastructure, to (i) learn the environment, (ii) quantify the accident risk, and (iii) adapt its backoff counter according to the risk. The results of this paper show that the proposed learning protocol is able to (i) evaluate an accident risk close to optimal and, as a result, (ii) yield a higher chance of transmission for a dangerous vehicle.

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“…Usage of redundant sensors on camera verification to avoid illusion and binding [35] P3 Jamming avoidance by making protective glasses around a LiDAR which acts as light filters [36] Vehicle-to-everything network P4 Usage of fog server with fog anonymizer to avoid eavesdropping in vehicular ad-hoc networks (VANETs) [20] P5 Maintaining data integrity in dynamic route guidance by forged data filtering scheme [25] P6 Using swarm algorithms for routing attacks [37] P7 Detecting bandwidth and entropy to reduce denial of service attack [38] P8 Implementing noisy control signals to avoid replay attacks [39] P9 Registering vehicles with TFD to avoid communication of attackers who are under victim identity [40] In-vehicle network P10 Encryption and cryptographic checksum to avoid proximity vulnerabilities [41] P11 Doing network segmentation to avoid CAN and SAE vulnerabilities [41] P12 Encryption and authentication to avoid flashing attacks [42]…”

Section: P2mentioning

confidence: 99%

A fsQCA-Based Framework for Cybersecurity of Connected and Automated Vehicles: Implications for Sustainable Development Goals

Karuppiah,

Sankaranarayanan,

Ali

et al. 2024

Vehicles

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Connected and automated vehicles (CAV) are increasingly recognized as a critical component of intelligent transportation systems (ITS), contributing to advances in transportation safety and mobility. However, the implementation of CAV in a real-world environment comes with various threats, and cybersecurity is among the most vulnerable. As the technology becomes more advanced and complex, it is essential to develop a comprehensive cybersecurity framework that can address these concerns. This research proposes a novel framework based on complexity theory and employs the fuzzy set qualitative comparative analysis (fsQCA) technique to identify combinations of security attacks that lead to achieving cybersecurity in CAV. Compared to structural equation modelling (SEM), the fsQCA method offers the advantage of demonstrating all possible ways to achieve the outcome. The study’s findings suggest that in-vehicle networks and data storage security are the most crucial factors in ensuring the cybersecurity of CAV. The results can be useful for automotive designers in reducing the potential for attacks while developing secure networks.

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“My autonomous car is an elephant”: A Machine Learning based Detector for Implausible Dimension

Cited by 13 publications

References 11 publications

CaTch: A Confidence Range Tolerant Misbehavior Detection Approach

CaTch: A Confidence Range Tolerant Misbehavior Detection Approach

Reinforcement Learning for Accident Risk-Adaptive V2X Networking

A fsQCA-Based Framework for Cybersecurity of Connected and Automated Vehicles: Implications for Sustainable Development Goals

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