Explaining RADAR features for detecting spoofing attacks in Connected Autonomous Vehicles

Rastogi, Nidhi; Rampazzi, Sara; Clifford, Michael N.; Heller, Miriam; Bishop, Matthew; Levitt, Karl N.

doi:10.48550/arxiv.2203.00150

Cited by 3 publications

(4 citation statements)

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“…LiDAR spoofing and jamming, signal analysis, relay, Radar spoofing and jamming. [11,[13][14][15][16]19].…”

Section: Lidar and Radarmentioning

confidence: 99%

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Cyber security analysis of connected vehicles

Mwanje,

Kaiwartya,

Aljaidi

et al. 2024

IET Intelligent Trans Sys

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The sensor‐enabled in‐vehicle communication and infrastructure‐centric vehicle‐to‐everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust‐based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area.

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“…LiDAR spoofing and jamming, signal analysis, relay, Radar spoofing and jamming. [11,[13][14][15][16]19].…”

Section: Lidar and Radarmentioning

confidence: 99%

“…Researchers in [11] perform experiments to investigate the accuracy of LiDAR and Radar data and suggest redundancy and randomization as countermeasures. More studies [14,15] have also evaluated the threats to LiDAR and Radar and can be referred to for further information.…”

Section: Lidar and Radarmentioning

confidence: 99%

Cyber security analysis of connected vehicles

Mwanje,

Kaiwartya,

Aljaidi

et al. 2024

IET Intelligent Trans Sys

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“…In context of X-IDS, the explanations generated by the explainer module may become a new point of attack for malicious actors. Attackers may add, delete, or modify explanations to evade detection [164]. Attackers may also attack training datasets to alter the explainer's behavior.…”

Section: Adverserial Aimentioning

confidence: 99%

Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities

et al. 2022

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The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity challenges has gained traction in industry and academia, partially as a result of widespread malware attacks on critical systems such as cloud infrastructures and government institutions. Intrusion Detection Systems (IDS), using some forms of AI, have received widespread adoption due to their ability to handle vast amounts of data with a high prediction accuracy. These systems are hosted in the organizational Cyber Security Operation Center (CSoC) as a defense tool to monitor and detect malicious network flow that would otherwise impact the Confidentiality, Integrity, and Availability (CIA). CSoC analysts rely on these systems to make decisions about the detected threats. However, IDSs designed using Deep Learning (DL) techniques are often treated as black box models and do not provide a justification for their predictions. This creates a barrier for CSoC analysts, as they are unable to improve their decisions based on the model's predictions. One solution to this problem is to design explainable IDS (X-IDS). This survey reviews the state-of-the-art in explainable AI (XAI) for IDS, its current challenges, and discusses how these challenges span to the design of an X-IDS. In particular, we discuss black box and white box approaches comprehensively. We also present the tradeoff between these approaches in terms of their performance and ability to produce explanations. Furthermore, we propose a generic architecture that considers human-in-the-loop which can be used as a guideline when designing an X-IDS. Research recommendations are given from three critical viewpoints: the need to define explainability for IDS, the need to create explanations tailored to various stakeholders, and the need to design metrics to evaluate explanations.INDEX TERMS Explainable intrusion detection systems, explainable artificial intelligence, machine learning, deep learning, white box, black box, explainability, cybersecurity.

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“…The CAN protocol, the mechanical and hydraulic systems used have been replaced by electronic network structures and control units. In this way, the amount of cables used has decreased, the cost has decreased, the connection structure has been simplified and the reliability of the system has increased [23]. It is the communication medium, also called low-speed CAN (Low-Speed CAN), where the electronic comfort units, where autonomous vehicles perform their functions, are connected, and non-real-time or non-critical data flows (Figure 4).…”

Section: Autonomous Vehicle Technologiesmentioning

confidence: 99%

Autonomous Vehicle Design in Lean Product Development Processes for Value Stream Map

Paker¹

2022

JTTs

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The global automotive industry is giving a difficult and common test in order to create advanced life models in the near future plans or scenarios that include current autonomous vehicle technologies. Therefore, the main purpose of the research is to comparatively evaluate the impact of autonomous vehicle technologies, which are newly included in the automotive manufacturing industry under sustainable competition, on lean product development processes, value acquisition and preservation, in different organizational structures in the approach. Although mergers or brand acquisitions in the global automotive industry create joint R & D (Research Development) or joint new P & D (Product Development) process structures for the development of autonomous vehicle technologies, heavy competition continues in the market. These new processes create different needs for the merger and partnership of the renewed traffic infrastructures under national and international regulations, and for the implementation of the new autonomous life model. Firm and brand marriages, mergers or acquisitions in today's automotive industry have ensured the high diffusion of lean product development processes under the stream of value creation or preservation carried out specific to the company under competition. Brand mergers in automotive industry companies struggling to survive under high competition create new work disciplines, professions, and engineering flow steps in lean product development processes. However, lean product development processes driven by technological innovation under simplification have resulted in the integration of parts and systems within the autonomous vehicle design structure, as well as creating new interdisciplinary value streams or different stakeholders. Therefore, the research revealed the significant effects of lean product development processes on the value stream in the automotive industry, on the mixed and lean product development process structure formed by new or existing vehicle systems (conventional vehicle) under the penetration of each existing and new discipline. This research compares the efficient operation steps of the process stake-How to cite this paper: Paker, F.A.

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Explaining RADAR features for detecting spoofing attacks in Connected Autonomous Vehicles

Cited by 3 publications

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Cyber security analysis of connected vehicles

Cyber security analysis of connected vehicles

Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities

Autonomous Vehicle Design in Lean Product Development Processes for Value Stream Map

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