# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 3 Generative AI for Threat Detection
### Transformers  
Transformers are a type of DL architecture introduced by [PERSON] et al. in 2017 in the groundbreaking paper \"Attention Is All You Need\" [62]. Unlike earlier models that relied heavily on recurrence (like RNNs) or convolution (like CNNs), Transformers process sequential data entirely through self-attention mechanisms. They were originally developed for machine translation but have since become the foundation for many state-of-the-art models in natural language processing (NLP), such as BERT, GPT, and T5. The transformer model is designed to handle variable-length input sequences and to capture long-range dependencies with greater efficiency than traditional sequence models. In the original paper [62], the transformer architecture is composed of an encoder and a decoder, each consisting of multiple identical layers--typically six in the base model. The encoder receives the input sequence and transforms it into a series of high-dimensional representations. The decoder, in turn, takes these representations and generates the output sequence, one element at a time, using both the previous output and the encoded input. Each layer in both the encoder and decoder contains two core components: a multi-head self-attention mechanism and a feed-forward neural network. The attention mechanism allows the model to weigh the importance of different tokens in the input sequence when computing each token's output representation. Multi-head attention, in particular, enables the model to focus on different positions in the sequence simultaneously, which helps capture a wide variety of dependencies and relationships. The output of these layers is passed through residual connections, followed by layer normalization to stabilize and speed up training. A key innovation in Transformers is the use of positional encodings to provide a sense of order to the input tokens, since the model does not inherently understand sequence order without recurrence. These encodings are added to the input embeddings and use sinusoidal functions that allow the model to learn and extrapolate positions within sequences. Figure 5: The architecture of the GAN. The Transformer architecture can be seen in Figure 6, where it is visually split into two halves--the encoder on the left and the decoder on the right. Each encoder layer contains a self-attention mechanism followed by a feed-forward network. Each decoder layer adds a third sub-layer for encoder-decoder attention, allowing the decoder to attend to the encoder's output. The overall structure is highly modular and parallelizable, which contributes significantly to faster training times compared to RNN-based models. This design achieves state-of-the-art results in tasks like machine translation while substantially reducing computational cost. In simple terms, the Transformer is a model that learns how words (or tokens) in a sentence relate to each other, without reading them one by one like traditional models. Instead, it looks at all words at once and decides which ones are important to focus on for understanding meaning. For example, when translating a sentence or detecting an anomaly, it can connect distant words or values that are related--even if they are far apart in the sequence. Its architecture enables it to process data faster and more accurately, making it especially powerful for language tasks and time series analysis in security applications. Figure 6: The architecture of the Transformer. In the context of cybersecurity, Transformers have demonstrated significant potential, particularly in detecting various types of attacks. Their ability to capture global context and complex patterns makes them ideal for analyzing sequential data such as system logs, network traffic, or command sequences, which are common in security-related tasks. Unlike traditional rule-based or statistical models that struggle with evolving threats, Transformers can learn from large datasets and generalize to unseen attack vectors.