## 2 Background and Related Work  
### Background  
#### 2.1.1 Cybersecurity Vulnerabilities in Autonomous and Connected Vehicular Systems  
Autonomous vehicles face several inherent vulnerabilities across their systems. electronic control units (ECUs) are susceptible to attacks due to their proprietary code, which can be re-flashed with modified firmware, tampered memory, or manipulated security keys, especially when physical access is gained. Onboard Diagnostics (OBD) ports are vulnerable to exploitation through the Controller Area Network (CAN) protocol, which lacks encryption and digital signatures, making it prone to replay and denial-of-service attacks. GPS sensors are exposed to manipulation due to open access to data and unencrypted civil signals, with weak terminal capabilities to distinguish false signals from real ones. LiDAR and ultrasonic sensors rely heavily on signal integrity, making them susceptible to spoofing and jamming attacks. Connected car systems, including Wi-Fi, Bluetooth, and mobile internet technologies, often lack robust security measures, while infotainment systems with accessible physical interfaces like USB and CD/DVD ports provide easy entry points for attackers. Firmware is at risk of extraction and decoding, exposing sensitive data such as encryption keys and intellectual property. Localization and navigation technologies, including GPS and HD maps, suffer from weak signal processing, authentication mechanisms, and insecure update frameworks. These vulnerabilities underscore the need for comprehensive cybersecurity measures to safeguard autonomous vehicle systems [24]. The security landscape of vehicular networks also presents a multifaceted array of vulnerabilities and attack vectors, critical for consideration in scientific discourse. In traditional vehicular ad hoc networks, inherent and external factors such as high mobility, real-time communication demands, decentralized architecture, and the heterogeneity of devices contribute to complex attack surfaces. At the physical layer, these networks are susceptible to jamming, eavesdropping, GPS spoofing, interference, node tampering, and impersonation attacks. The data link layer faces threats including traffic analysis, beaconing, replay, identity spoofing, collision, resource exhaustion, bit flipping, and man-in-the-middle attacks. Network layer vulnerabilities encompass Sybil, wormhole, replay, black hole, gray hole, routing table overflow, impersonation, location disclosure, tunneling, packet dropping, malicious flooding, node impersonation, collusion, and routing loop attacks. At the transport layer, vehicular networks face attacks like session hijacking, man-in-the-middle, and various forms of denial of service (DoS). The application layer is vulnerable to malware, phishing, data tampering, and privacy breaches. The emergence of Software-Defined Networking (SDN) in vehicular networks introduces further security challenges due to its complex architecture, centralized control plane, and dynamic topology. SDN-based systems are susceptible to attacks across their data, control, and application planes, including spoofing, flow table overflows, controller compromises, DoS, API exploitation, and cross-layer attacks [25]. The cybersecurity landscape of modern vehicular systems is complex, with various attacks directly correlating to identified vulnerabilities. Blinding and jamming attacks, which involve saturating detectors with infrared light pulses or interfering with laser signals, exploit the reliance of LiDAR and ultrasonic sensors on signal integrity. Network-centric threats, such as black hole attacks, manifest as disruptions where data packets are dropped, preventing their intended network traversal. Similarly, timing attacks introduce delays in time-sensitive communications, thereby disrupting vehicle coordination and safety-critical applications.