# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 2 Background
### Review of Existing ML and DL Security Approaches in IoV
#### 2.5.2 Deep Learning-Based IDS
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DL Model** & **Reference** & **Detection Target** & **Strengths and Limitations** \\ \hline MLP & (2018) & DoS attack, U2R attack, R2L attack, R2L attack, Probe attack & \(\blacktriangledown\)High accuracy with simple design \\  & (2018) & General malicious packet injection attacks & \(\blacktriangledown\)High accuracy and real-time detection \\  & (2018) & CAN bus signal anomalies & \(\blacktriangledown\)High detection accuracy, robust to external intrusion attacks \\  & (2018) & CAN malicious attacks & \(\blacktriangledown\)Cannot detect attacks originating from internal ECUs \\ \cline{2-4}  & (2018) & CAN malicious attacks & \(\blacktriangledown\)Tested on real data from 3 vehicles \\  & (2018) & CAN malicious attacks & \(\blacktriangledown\)Complexity in embedded setups \\ \hline DBN & (2018) & VANET intrusions & \(\blacktriangledown\)Good for classification, mitigates overfitting \\  & (2018) & VANET intrusion & \(\blacktriangledown\)Rarely used independently, slower \\ \hline CNN & (2018) & Real-time intrusion detection at vehicle terminals & \(\blacktriangledown\)Lightweight, real-time detection \\  & (2018) & CAN bus intrusion & \(\blacktriangledown\)Limited handling of temporal data \\ \cline{2-4}  & (2018) & CAN bus intrusion & \(\blacktriangledown\)Effective online/offline detection \\  & (2018) & CAN bus intrusion & \(\blacktriangledown\)Requires external CAN devices \\ \hline DCNN & (2018) & DDoS attack & \(\blacktriangledown\)High accuracy, good for RSU traffic \\  & (2018) & DDoS attack & \(\blacktriangledown\)Latency not addressed \\ \hline LSTM & (2018) & Fuzzy, RPM, DOS, GEAR attacks & \(\blacktriangledown\)Good for sequential data, multiple intrusion types \\  & (2018) & CAN bus attacks & \(\blacktriangledown\)Resource-intensive \\ \cline{2-4}  & (2018) & CAN bus attacks & \(\blacktriangledown\)High accuracy \\  & (2018) & CAN bus attacks & \(\blacktriangledown\)High latency (128 ms), large overhead \\ \hline Bi-LSTM + DNN + Bloom Filter & (2018) & Internal and external IoV attacks & \(\blacktriangledown\)Short training, low detection latency \\  & (2018) & CAN bus attacks & \(\blacktriangledown\)Lacks FPR and FNR metrics \\ \hline CNN, LSTM & (2018) & Wormhole, Sybil, DoS, Infiltrating Transfer, DDoS, Black-hole, Brute Force attacks & \(\blacktriangledown\)Efficient, high detection performance \\  & (2018) & CAN bus attacks & \(\blacktriangledown\)Model complexity \\ \hline CNN-LSTM, MLP, LSTM, CNN & (2018) & Disruptive, Replay, DoS, Sybil, Hybrid & \(\blacktriangledown\)High accuracy, low latency, MEC-friendly \\ \hline HTM & (2018) & CAN anomaly detection & \(\blacktriangledown\)Struggles with similar attack types \\  & (2018) & CAN bus attacks & \(\blacktriangledown\)Outperforms RNN and HMM in precision and recall \\  & (2018) & CAN bus attacks & \(\blacktriangledown\)Relies on synthetic attack data \\ \hline RNN-LSTM with cloud offloading & (2018) & Cyberattacks & \(\blacktriangledown\)Outperforms traditional classifiers in accuracy \\  & (2018) & CAN bus attacks & \(\blacktriangledown\)Limited to robotic testbed \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Adaptive comparative summary of DL-based IDS for IoV. [PERSON] et al. [47] proposed a two-stage intrusion detection system combining rule-based logic and a deep neural network (DNN) for real-time attack detection on the CAN bus. The first stage uses lightweight, rule-based techniques to rapidly identify violations in message periodicity and regularity, reducing the computational load on the second stage. The second stage, based on a DNN, is designed to detect attacks missed by the initial filter. The system was evaluated using real CAN traffic collected from three different vehicles--a Honda Accord, an Asia-brand vehicle, and a US-brand vehicle. Results showed high effectiveness, with detection rates between 99.91% and 99.97% and per-message detection times ranging from 0.53 ms to 0.61 ms.  
On the other hand, [PERSON] et al.