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@article{195582,
        author = {Parth Joshi and Aryan Kale and Soham Bodhani and Manas Beke and Dinesh Bawane},
        title = {AI-DRIVEN SECURITY: PROTECTING AUTONOMOUS VEHICLES FROM CYBER THREATS AND HACKING RISKS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4103-4110},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195582},
        abstract = {This research study looks into the cybersecurity threats posed by autonomous vehicles and explores using advanced artificial intelligence (AI) techniques as useful countermeasures against such a threat. Autonomous vehicles rely in significant part upon a highly complex system of sensors and actuators constantly interacting with software modules in making real-time driving decisions. 
These components—spanning from LiDAR, radar, ultrasonic sensors, cameras, to GPS systems—form the vehicle's sensory spine. But with their susceptibility to deception, they also carry great threats. Malicious actors can, for instance, exploit sensor spoofing to introduce artificial environmental data, which would be part of navigation calculation errors, obstacle detection, or lane positioning. Actuators, in charge of commanding steering, accelerating, braking, and other mechanical responses, may also be remotely seized by injections of illicit orders, leading to physical loss of control or sinister action. 
Even from a security perspective, decentralized and networked character of the kind of system makes available extensive attack surfaces. Wireless communication systems like V2V (vehicle-to- vehicle) and V2I (vehicle-to-infrastructure) expose vehicles to interception, spoofing, and denial-of- service (DoS) attacks, given that encryption and authentication processes are weak or out of date. In response to such attacks, this study proposes a smart, multi-layered defence mechanism. AI- assisted methods like hybrid graph-based reinforcement learning are employed in dynamic sensor integrity verification and real-time trust scoring.},
        keywords = {Self-Driving Cars (SDCs), - Artificial Intelligence (AI), - Cybersecurity, - Hacking Risks, - Intrusion Detection Systems, - Sensor Manipulation, - Secure Connectivity, - Adversarial AI, - Vehicle-to-Everything (V2X) Communication, - Blockchain Security, - Quantum Computing, AI-Driven Security: Protecting Autonomous Vehicles from Cyber Threats and Hacking Risks},
        month = {April},
        }