# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 3 Generative AI for Threat Detection
### Variational Autoencoders (VAEs)  
Variational Autoencoders, introduced by [PERSON] et al. ([PERSON] et al., 2016), are a class of deep generative models designed to learn latent representations of data in a probabilistic framework. Unlike classical Autoencoders that map input data to a fixed code, VAEs encode each input into a distribution over the latent space, typically parameterized as a Gaussian. This allows for efficient sampling and generation of new data points. The learning objective of a VAE is derived from variational inference, where the model aims to maximize the likelihood of the data while regularizing the latent space to follow a known prior distribution. This is accomplished by optimizing the Evidence Lower Bound (ELBO), which balances reconstruction quality with the closeness of the learned latent distribution to the prior ([PERSON] et al., 2016). VAEs have been widely adopted for tasks such as image synthesis, anomaly detection, and unsupervised representation learning due to their flexibility and strong mathematical foundations.  
In terms of architecture, VAEs are composed of two neural networks: an encoder and a decoder. The encoder receives the input data and outputs the parameters (mean and variance) of a latent Gaussian distribution. A latent vector is then sampled from this distribution using the reparameterization trick, which allows gradients to flow through the sampling operation during backpropagation. The decoder then attempts to reconstruct the original input from the sampled latent vector. This process forces the model to learn compact, informative latent representations. As illustrated in Figure 7 of the referenced paper ([PERSON] et al., 2016), the VAE architecture includes a stochastic component where random noise sampled from a standard normal distribution is transformed using the encoder's outputs to generate the latent variable. This sampled variable is passed to the decoder for reconstruction. The overall structure allows for both efficient encoding of inputs and flexible generation of new data by sampling directly from the latent space.  
Figure 7: The architecture of the VAE ([PERSON] et al., 2016).  
VAE can be thought of as a smart compressor and generator. It first compresses input data (like images or messages) into a smaller abstract form (called a latent representation), but instead of producing a single fixed output, it learns a range of possible representations using probability. This allows the model not only to recreate the original data but also to generate new, similar data by sampling from that learned range. This probabilistic nature makes VAEs particularly effective for detecting unusual patterns, such as anomalies in security systems, or generating realistic synthetic data for training purposes.  
VAEs also offer powerful capabilities in the field of cybersecurity, especially for attack detection in complex and dynamic environments. Since VAEs are generative models trained to learn the normal distribution of data, they can be employed to detect anomalies that deviate from expected behavior. In security applications such as intrusion detection systems, VAE can be trained on benign network traffic or system logs. During deployment, any input that significantly deviates from the learned distribution--indicated by a high reconstruction error or a low likelihood under the latent space--can be flagged as potentially malicious. This makes VAEs highly effective for detecting novel attacks, zero-day exploits, or subtle deviations in behavior that rule-based systems may overlook. Furthermore, the flexibility of VAEs allows them to be adapted to various types of data, including packet flows, system events, or even sensor data in IoV environments. By capturing the underlying structure of normal behavior, VAEs provide a robust and unsupervised approach to identifying cybersecurity threats without requiring labeled attack data.