RTIDS: A Robust Transformer-Based Approach for Intrusion Detection System

Due to the rapid growth in network traf c and increasing security threats, Intrusion Detec tion Systems (IDS) have become increasingly critical in the eld of cyber security for providing secure communications against cyber adversaries. However, there exist many challenges for designing a robust, ef cient and accurate IDS, especially when dealing with high-dimensional anomaly data with unforeseen and unpredictable attacks. In this paper, we propose a Robust Transformer-based Intrusion Detection System(RTIDS)reconstructingfeaturerepresentationstomakeatrade-offbetweendimensionalityreduction and feature retention in imbalanced datasets. The proposed method utilizes positional embedding technique to associate sequential information between features, then a variant stacked encoder-decoder neural network is used to learn low-dimensional feature representations from high-dimensional raw data. Furthermore, we apply self-attention mechanism to facilitate network traf c type classi cations. Extensive experiments reveal the effectiveness of the proposed RTIDS on two publicly available real traf c intrusion detection datasets named CICIDS2017 and CIC-DDoS2019 with F1-Score of 99.17% and 98.48% respectively. A comparative study with classical machine learning algorithm support vector machine (SVM) and deep learning algorithms that include recurrent neural network (RNN), fuzzy neural network (FNN), and long short-term memory network (LSTM) is conducted to demonstrate the validity of the proposed method.