The design of a defence mechanism to mitigate the spectrum sensing data falsification attack in cognitive radio ad hoc networks

Connected Autonomous Vehicles (CAVs) are transformational technologies that have demonstrated significant potential for shaping the future of transportation systems. CAV research has been conducted extensively, both in academic and industrial pursuits. The intention was to put CAVs on the road, but the safety and efficiency of CAVs must be prioritized for this purpose to come to fruition. This technology is built upon sensors, and network communications have the potential to improve automotive infrastructure, reduce traffic and accidents, and facilitate a unified transportation system. Although the auspiciousness of these vehicles is clear, persistent threats exist in terms of cybersecurity attacks, which jeopardize the safety and effectiveness of CAVs. Our study provides a comprehensive dissection of cyberattacks and digital forensics on CAVs. We begin by discussing each element of a standard CAV network and then illustrate the current security. The three main components of CAVs-sensors, communication networks, and actuators-were analyzed in detail. The expansion of cybersecurity and forensic issues is presented with additional investigations into traditional and artificial intelligence-based cyber-defense techniques. Our work concludes by discussing the open challenges and potential research areas for developing robust cybersecurity and forensic solutions exclusively for CAVs.

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The design of a defence mechanism to mitigate the spectrum sensing data falsification attack in cognitive radio ad hoc networks

Cited by 5 publications

References 41 publications

Addressing the Effects of the Spectrum Sensing Data Falsification Attack Using the Enhanced Q-out-of-m Rule

Addressing the Effects of the Spectrum Sensing Data Falsification Attack Using the Enhanced Q-out-of-m Rule

Spectrum Sensing Data Falsification Attack Reputation and Q-Out-of-M Rule Security Scheme

Cybersecurity and Forensics in Connected Autonomous Vehicles: A Review of the State-of-the-Art

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