## 2 Background
### An Overview of Intrusion Detection Systems
#### 2.3.3 Internet of Vehicle Intrusion Detection  
Within the dynamic and constantly changing domain of the IoV, IDSs play a vital role as digital protectors, safeguarding the resilience of interconnected vehicular networks against an increasingly diverse range of cyberattacks. Fundamentally, an IDS in an IoV setting entails a multifaceted approach that involves behavioral analysis, signature-based detection, and anomaly-based detection. Behavioral analysis is a fundamental aspect that involves careful observation and a comprehensive understanding of the complex patterns exhibited by vehicle behavior and network connections [47]. By effectively distinguishing between typical and atypical behaviors, the system can immediately detect deviations, therefore flagging possible intrusions or harmful operations. Simultaneously, signature-based detection functions as the initial layer of protection. This approach entails comparing incoming data with an extensive database of identified attack patterns. When a match occurs, it initiates an alert, facilitating prompt remedial action. Anomaly-based detection, a more advanced technique, creates baselines of typical behavior. When anomalies--such as atypical data traffic or unauthorized system access--are identified, alerts are sent, facilitating proactive measures to address potential security risks [48].  
Furthermore, within the context of the IoV, ensuring the security of vehicle-to-everything communication is critical. Establishing robust cryptographic protocols is necessary to safeguard the complex communication network between vehicles and outside entities. These protocols play a crucial role in guaranteeing the secrecy, integrity, and validity of the data being communicated. Incorporating physical and cybersecurity measures provides an enhanced level of safeguarding. The detection systems for physical tampering serve to notify the IDS of potential threats, facilitating proactive cybersecurity measures [6]. By harnessing the capabilities of machine learning-based detection, IDSs can dynamically adjust and evolve. Machine learning algorithms, specifically deep learning models, can analyze extensive datasets obtained from car sensors and network interactions. This enablesthe detection of subtle patterns that can serve as indicators of cyber risks, including those previously unidentified.  
Significantly, the implementation of real-time threat response mechanisms dramatically enhances the effectiveness of IDSs in IoV. Real-time notifications, activated by irregularities or suspected breaches, are received by individuals inside the vehicle, managers overseeing the fleet, and centralized monitoring systems. These notifications prompt swift and targeted actions, including measures such as network segment isolation and emergency protocol activation. These actions effectively contain threats and safeguard the overall integrity of the network. The IDSs employed in the IoV encompass a complex integration of several components, including behavioral analysis, pattern identification, cryptographic techniques, machine learning capabilities, and instantaneous reactions. These technologies ensure secure data transmission inside the IoV and protect the safety, privacy, and trust of all individuals connected to this complex vehicular network. In doing so, they strengthen the fundamental basis upon which the future of transportation technology relies.