POSTER: Anomaly-based misbehaviour detection in connected car backends

Berlin, Olga; Held, Albert; Matousek, Matthias; Kargl, Frank

doi:10.1109/vnc.2016.7835978

Cited by 12 publications

(6 citation statements)

References 4 publications

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“…These attacks include Denial Of Service (DOS) attack, internal attacks in driverless cars, packet dropping attacks, Gray hole, Rushing attacks, and Sybil attacks. Berlin et al [94] introduced a DL based technique for anomaly detection in a single vehicle or a fleet of vehicles. This technique is suitable for attacks with stolen credentials in a privacy-friendly approach.…”

Section: E Security In Vanetmentioning

confidence: 99%

Artificial Intelligence for Vehicle-to-Everything: A Survey

et al. 2019

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Recently, the advancement in communications, intelligent transportation systems, and computational systems has opened up new opportunities for intelligent traffic safety, comfort, and efficiency solutions. Artificial intelligence (AI) has been widely used to optimize traditional data-driven approaches in different areas of the scientific research. Vehicle-to-everything (V2X) system together with AI can acquire the information from diverse sources, can expand the driver's perception, and can predict to avoid potential accidents, thus enhancing the comfort, safety, and efficiency of the driving. This paper presents a comprehensive survey of the research works that have utilized AI to address various research challenges in V2X systems. We have summarized the contribution of these research works and categorized them according to the application domains. Finally, we present open problems and research challenges that need to be addressed for realizing the full potential of AI to advance V2X systems.

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Section: E Security In Vanetmentioning

confidence: 99%

Artificial Intelligence for Vehicle-to-Everything: A Survey

et al. 2019

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“…Additionally, some scientific publications emphasizing SIEMs and fleet-wide analysis are mentioned in the following. Berlin et al introduce SeMaCoCa (Security Management of Services in Connected Cars), a SIEM system that aims to detect misbehavior based on anomaly detection using machine learning [91]. However, in addition to vehicle data, the authors mention various external context data sources that could improve the detection process, e.g.…”

Section: A Fleet Securitymentioning

confidence: 99%

“…It is based on a specific adaption of the machine-learning technique Isolation Forest to reduce false alarms, which are familiar to anomaly-based IDS[90]. Additionally, they suggest integrating vulnerability information as context / CTI from the Auto-ISAC[62].2) ''POSTER: ANOMALY-BASED MISBEHAVIOUR DETECTION IN CONNECTED CAR BACKENDS''[91]…”

mentioning

confidence: 99%

Context-Aware Security for Vehicles and Fleets: A Survey

2021

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Vehicles are becoming increasingly intelligent and connected. Interfaces for communication with the vehicle, such as WiFi and 5G, enable seamless integration into the user's life, but also cyber attacks on the vehicle. Therefore, research is working on in-vehicle countermeasures such as authentication, access controls, or intrusion detection. Recently, legal regulations have also become effective that require automobile manufacturers to set up a monitoring system for fleet-wide security analysis. The growing amount of software, networking, and the automation of driving create new challenges for security. Context-awareness, situational understanding, adaptive security, and threat intelligence are necessary to cope with these everincreasing risks. In-vehicle security should be adaptive to secure the car in an infinite number of (driving) situations. For fleet-wide analysis and alert triage, knowledge and understanding of the circumstances are required. Context-awareness, nonetheless, has been sparsely considered in the field of vehicle security. This work aims to be a precursor to context-aware, adaptive and intelligent security for vehicles and fleets. To this end, we provide a comprehensive literature review that analyzes the vehicular as well as related domains. Our survey is mainly characterized by the detailed analysis of the context information that is relevant for vehicle security in the future.

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“…Olga Berlin et al [54] introduced a Security Information and Event Management System (SIEM) called Security Management of Services in Connected Cars (SeMaCoCa). The proposed system uses data from vehicles (e.g., odometer values) as well as additional information from other sources (e.g., data from third parties and service garages) to recognise attacks.…”

Section: ) Others/hybridmentioning

confidence: 99%

Intrusion Detection Systems for Intra-Vehicle Networks: A Review

et al. 2019

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A modern vehicle is a complex system of sensors, electronic control units, and actuators connected through different types of intra-vehicle networks to control and monitor the state of the vehicle. In addition, modern vehicles are becoming increasingly connected to the outside world through V2X technologies. However, these provide new attack surfaces that increase the cybersecurity risk to modern vehicles. To this end, there are two distinct and key challenges that need to be addressed to ensure safety and consumer trust. While modern vehicles must be equipped with the best countermeasures against cybersecurity threats, a reliable mechanism shall be also in place to detect the potential intrusions of the system while in operation, which is termed as intrusion detection. This paper provides a structured and comprehensive review of the state of the art of the intra-vehicle intrusion detection systems (IDSs) for passenger vehicles. We first provide an overview of intra-vehicle networks before reviewing contemporary research in intra-vehicle IDSs. The approach employed is to categorize the reviewed works based on their detection technique and to examine the used feature and feature selection methods, evaluation dataset, attack type, performance metrics, and benchmark models. This paper also presents outstanding research challenges and gaps in intra-vehicle IDS research.

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POSTER: Anomaly-based misbehaviour detection in connected car backends

Cited by 12 publications

References 4 publications

Artificial Intelligence for Vehicle-to-Everything: A Survey

Artificial Intelligence for Vehicle-to-Everything: A Survey

Context-Aware Security for Vehicles and Fleets: A Survey

Intrusion Detection Systems for Intra-Vehicle Networks: A Review

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