Shadows Don't Lie: n-Sequence Trajectory Inspection for Misbehaviour Detection and Classification in VANETs

Le, Anhtuan; Maple, Carsten

doi:10.1109/vtcfall.2019.8891137

Cited by 18 publications

(19 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They designed plausibility metrics with six features: (i) local plausibility check: sender's location is compared with a predicted plausible location and the distribution of average acceleration; (ii) movement plausibility check: this feature check the plausibility of the total displacement with the average velocity during the entire trip and compare with total displacement; (iii) quantitative features: these features are numerical description of the vehicle behavior, which represents the difference between the calculated average velocities based on total displacement, time, and the predicted average velocity. Le and Maple [8] suggested ML approaches to detect misbehavior in vehicular networks based on n-sequence trajectory inspection where a sequence of messages was considered to form a trajectory [11]. Three features were used to extract the data: (i) movement plausibility check: focus where the vehicle is moving but reported as uncharged, by observing a sequence of trajectories; (ii) minimum distance to trajectories: aims to find moving patterns of the vehicle in the legitimate set; and (iii) minimum translation to the trajectories.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, vehicular networks have benefited from the advances in machine learning in the areas of network security. Indeed, several ML-based Misbehavior Detection Systems (ML-based MDSs) have been proposed for the efficient detection of false position attacks [5][6][7][8][9][10]. However, existing solutions leverage numerous features which increase the computational complexity and overhead.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Misbehavior Detection System for Detecting False Position Attacks in Vehicular Networks

Hawlader

Boualouache

Faye

et al. 2021

2021 IEEE International Conference on Communications Workshops (ICC Workshops)

View full text Add to dashboard Cite

Position falsification attacks are one of the most dangerous internal attacks in vehicular networks. Several Machine Learning-based Misbehavior Detection Systems (ML-based MDSs) have recently been proposed to detect these attacks and mitigate their impact. However, existing ML-based MDSs require numerous features, which increases the computational time needed to detect attacks. In this context, this paper introduces a novel ML-based MDS for the early detection of position falsification attacks. Based only on received positions, our system provides real-time and accurate predictions. Our system is intensively trained and tested using a publicly available data set, while its validation is done by simulation. Six conventional classification algorithms are applied to estimate and construct the best model based on supervised learning. The results show that the proposed system can detect position falsification attacks with almost 100% accuracy.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Misbehavior Detection System for Detecting False Position Attacks in Vehicular Networks

Hawlader

Boualouache

Faye

et al. 2021

2021 IEEE International Conference on Communications Workshops (ICC Workshops)

View full text Add to dashboard Cite

show abstract

“…Le and Maple. [14] proposed a supervised learning approach to detect false position attacks. This approach compares the trajectory of vehicles with trajectories of legitimated vehicles.…”

Section: B Ml-based Misbehavior Detection Systemsmentioning

confidence: 99%

“…Recently, vehicular networks have benefited from the advances in machine learning in the areas of network security. Indeed, several ML-based Misbehavior Detection Systems (MLbased MDSs) have been proposed for the efficient detection of internal attackers [12][13][14][15][16][17][18][19][20]. However, existing solutions have only been proposed to detect active attackers since detecting passive attacks is usually reported as impossible [21].…”

Section: Introductionmentioning

confidence: 99%

Federated learning-based scheme for detecting passive mobile attackers in 5G vehicular edge computing

Boualouache

Engel

2021

Ann. Telecommun.

View full text Add to dashboard Cite

Detecting passive attacks is always considered difficult in vehicular networks. Passive attackers can eavesdrop on the wireless medium to collect beacons. These beacons can be exploited to track the positions of vehicles not only to violate their location privacy but also for criminal purposes. In this paper, we propose a novel federated learning-based scheme for detecting passive mobile attackers in 5G Vehicular Edge Computing. We first identify a set of strategies that can be used by attackers to efficiently track vehicles without being visually detected. We then build an efficient Machine Learning (ML) model to detect tracking attacks based only on the receiving beacons. Our scheme enables Federated Learning (FL) at the edge to ensure collaborative learning while preserving the privacy of vehicles. Moreover, FL clients use a semi-supervised learning approach to ensure accurate self-labeling. Our experiments demonstrate the effectiveness of our proposed scheme to detect passive mobile attackers quickly and with high accuracy. Indeed, only 20 received beacons are required to achieve 95% accuracy. This accuracy can be achieved within 60 FL rounds using 5 FL clients in each FL round. The obtained results are also validated through simulations.

show abstract

“…VANET [36]- [68] IoV [69]- [78] SDVN [37], [38], [53] [62], [70], [79] EEVN [57], [58], [80] VCC [48], [51] [53], [81] 5GVN [82] Fig. 3.…”

Section: Vehicular Networkmentioning

confidence: 99%

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Talpur,

Gurusamy

2021

Preprint

View full text Add to dashboard Cite

Machine Learning (ML) has emerged as an attractive and viable technique to provide effective solutions for a wide range of application domains. An important application domain is vehicular networks wherein ML-based approaches are found to be very useful to address various problems. The use of wireless communication between vehicular nodes and/or infrastructure makes it vulnerable to different types of attacks. In this regard, ML and its variants are gaining popularity to detect attacks and deal with different kinds of security issues in vehicular communication. In this paper, we present a comprehensive survey of ML-based techniques for different security issues in vehicular networks. We first briefly introduce the basics of vehicular networks and different types of communications. Apart from the traditional vehicular networks, we also consider modern vehicular network architectures. We propose a taxonomy of security attacks in vehicular networks and discuss various security challenges and requirements. We classify the ML techniques developed in the literature according to their use in vehicular network applications. We explain the solution approaches and working principles of these ML techniques in addressing various security challenges and provide insightful discussion. The limitations and challenges in using ML-based methods in vehicular networks are discussed. Finally, we present observations and lessons learned before we conclude our work.

show abstract

Shadows Don't Lie: n-Sequence Trajectory Inspection for Misbehaviour Detection and Classification in VANETs

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 18 publications

References 7 publications

Intelligent Misbehavior Detection System for Detecting False Position Attacks in Vehicular Networks

Intelligent Misbehavior Detection System for Detecting False Position Attacks in Vehicular Networks

Federated learning-based scheme for detecting passive mobile attackers in 5G vehicular edge computing

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Contact Info

Product

Resources

About