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The Dark Side of The Internet of Vehicles: A Survey of the State of IoV and its Security Vulnerabilities

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arxiv 2211.05775 v1 pith:TDYHV6RL submitted 2022-11-07 cs.CR cs.SYeess.SY

classification cs.CRcs.SYeess.SY
keywords researchfieldthreatsvehiclesvulnerabilitiesadditionaddressattacks
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For the smart vehicular network, we studied two technologies to realize it. The first technology is the cooperative scheme which improves capacity by properly combining the V2V and V2I. The second technology is an online learning algorithm which can deal with the beam selection problem in mmWave system. Both are effective and can be used in autonomous driving systems. However, advancements in the field of IoV have elicited research in different areas related to the field. This highlights a critical need to address security and protection challenges as a result of the progression of vehicles and everything that is being transferred to the internet. In addition, to understand exactly where research is missing regarding IoV, we found that a survey of current research in the vulnerabilities and threats to general IoT applications. In addition to other attacks, we found that DDoS attacks in the form of botnets are significant threats to the IoT world. Upon researching which threats and vulnerabilities are leveraged in IoV research, the field was severely lacking in botnet and DDoS attack research. If developers neglect to address this issue before interconnected vehicles become a mainstream reality, this discovery can have severe ramifications for the safety of IoV consumers around the globe.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FAPL-DM-BC: A Secure and Scalable FL Framework with Adaptive Privacy and Dynamic Masking, Blockchain, and XAI for the IoVs

    cs.CR 2025-01 reject novelty 3.0 of 10

    The paper sketches an untested architecture integrating adaptive privacy, dynamic masking, blockchain provenance, secure aggregation, and XAI for IoV federated learning.

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