# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 2 Background
### Emerging AI-Driven Threats in IoT and Implications for IoV Security  
Recent developments in AI-based attack strategies within general IoT systems have revealed a range of sophisticated side-channel and generative techniques that pose significant security risks--many of which carry direct implications for the Internet of Vehicles (IoV). For instance, [PERSON] et al. [19] introduced MagInspector, an unsupervised learning based detection mechanism that captures GPU magnetic side-channel patterns to identify cryptojacking behaviors, achieving significant accuracy improvements over supervised baselines. Similarly, MagSign, as introduced in Ref. [20], leverages dynamic magnetic fields generated by IoT screens to perform low-cost user authentication using ambient magnetism, demonstrating the potential of magnetism as both an identifier and a potential vector for spoofing if exploited.  
Other research has revealed novel side-channel attacks through ubiquitous IoT hardware. [PERSON] et al. [21] proposed WISERS, which exploits coil white and magnetic field perturbations during wireless charging to infer user interactions--such as screen unlocking--with over 90% accuracy. Subsequent work, AppListener, as proposed in Ref. [22], showed that radio frequency (RF) energy harvested from Wi-Fi transmissions can be used to detect fine-grained mobile app activity, with classification accuracies exceeding 98%. Additionally, FPLogger, presented in Ref. [23], demonstrated that electromagnetic emissions from in-display fingerprint sensors can be leveraged to reconstruct fingerprint images and conduct successful spoofing attacks on modern smartphones.  
Generative models are also playing a dual role in both attacks and defenses. The AccEar framework, described in Ref. [24], employs a conditional GAN to reconstruct unconstrained speech from accelerometer data, bypassing microphone permissions and exposing audio leakage vulnerabilities in voice-based systems. Meanwhile, the F2 Key system, proposed in Ref. [25], applies generative modeling to embed user-specific facial features and vocal patterns into a private key, achieving strong resistance against spoofing and replay attacks in wearable devices.  
Beyond hardware- and sensor-level threats, Large Language Models (LLMs) introduce further risks. [PERSON] et al. [26] presented a detailed taxonomy of backdoor attacks in LLMs, highlighting vulnerabilities introduced during training and outlining potential defenses. Complementing this, [PERSON] et al. [27] surveyed the use of LLMs in program analysis, emphasizing their role in automating code comprehension and identifying security flaws--while also introducing new avenues for exploitation if misused.  
Although these works are situated in general purpose IoT and mobile contexts, they reveal evolving attack surfaces and capabilities that are increasingly transferable to vehicular systems. Side-channel vulnerabilities, generative model-driven spoofing, and LLM manipulation all exemplify the complexity of future threats. These insights reinforce the need for adaptive, intelligent, and generative IDS frameworks that can address not only classical intrusion patterns but also emerging, multi-modal, and context-aware attack vectors in IoV environments.