# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 5 Challenges and Future Research Directions  
### Challenges  
Generative AI-based IDSs for the Internet of Vehicles face several pressing challenges that hinder their practical deployment and effectiveness. A primary concern is the high computational and memory requirements of generative models, such as GANs and VAEs. These models demand extensive processing power and are not well suited to the resource-constrained nature of vehicular edge devices. In addition, their longer training and inference times pose difficulties for real-time intrusion detection, where split-second responses are often necessary to ensure passenger safety and vehicle functionality. Moreover, the absence of vehicle-grade hardware acceleration tailored to support complex deep learning architectures further limits their deployment. Most in-vehicle processors lack dedicated AI acceleration units (e.g., NPUs or GPUs), making it impractical to run generative models locally without major architectural redesigns. Another major challenge is accurately distinguishing between malicious behavior and benign system anomalies. In IoV environments, abnormal data patterns may stem from non-malicious causes such as sensor drift, hardware failures, or environmental factors. However, generative models often struggle to differentiate these from actual cyberattacks, resulting in false positives that reduce trust in IDS alerts. This challenge is intensified by privacy concerns, as many AI models rely on centralized data collection and processing, which risk exposing sensitive vehicle or user information when data is transmitted to cloud servers. This problem is compounded by V2X communication bandwidth limitations, which can delay or restrict the transmission of real-time IDS outputs or model updates. Such constraints are especially critical in dense urban environments or high-speed traffic scenarios, where latency directly impacts the system's ability to respond to threats in time. Further complicating the issue is the limited availability of high-quality, real-world datasets for training and evaluating IDS. Most existing models are built and tested using simulated or synthetic data, which may not capture the full range of threats and behaviors encountered in real driving scenarios. Only a few studies, such as [39; 43; 47], have validated their methods using data collected from actual vehicular systems, including real CAN traffic or physical testbeds. However, these deployments are generally small scale and focus on conventional ML-based IDS, not generative models. As such, a clear gap persists between academic research and large-scale, production-grade integration of Generative AI into vehicle systems. This highlights the urgent need for future research to progress beyond simulation and lab validation toward industry-aligned pilot deployments. In addition, there is a scarcity of publicly available datasets that capture adverse driving conditions such as weather-induced sensor noise, nighttime scenarios, or rare failure events, which limits the generalizability of IDS models across diverse operational contexts. Additionally, the absence of standard evaluation benchmarks and large-scale real-world testing platforms makes it difficult to assess how these systems will perform under dynamic conditions, such as mixed traffic environments involving both autonomous and human-driven vehicles. Integration with existing vehicle architectures presents another bottleneck. Modern vehicles are governed by legacy ECUs and domain-specific network protocols, which often lack the flexibility to accommodate the high-bandwidth data pipelines and low-latency inference engines needed for Generative AI. Seamless incorporation of IDS componentswithout disrupting functional safety or system determinism remains a significant engineering hurdle. Real-world deployment of Generative AI models in safety-critical automotive systems is also constrained by the absence of clear certification pathways. Compliance with standards such as ISO/SAE 21,434 [93] and UN Regulation No.