Transformers model for DDoS attack detection: A survey

Abstract

Distributed Denial of Service (DDoS) attack detection through Transformer models is one of the innovative Deep Learning applications. DDoS attacks are hard to handle and there is no definitive solution. Therefore, detecting DDoS attacks based on the Transformer architecture are being widely explored because of its versatility and customization. Transformer architectures analyze network traffic and identify malicious patterns, given different advantages from these architectures, such as the processing capacity in long sequences, the attention mechanism (a.k.a., self-attention) aimed at capturing complex patterns in the identification of malicious traffic, real-time detection through parallelism, the generalization to new types of attacks and, finally, the complete integration with other artificial intelligence techniques. Therefore, this survey is an extensive literature review providing an overview of the Transformer Architecture through different applied models, strategies for data preprocessing, and applications in various types of data, including real-time, address different machine learning techniques and deep learning. Thus, it analyzed 45 papers that focus on detecting DDoS attacks. The F1-Score of the DDoS attack detection identified in the papers varies between 47.40% and 100.00%. This survey contributes to the understanding of relevant aspects in different models applied in transformer architecture and thus emphasizes open issues and research directions.