LAUSR

Due to the rapid growth in network traffic and increasing security threats, Intrusion Detection Systems (IDS) have become increasingly critical in the field of cyber security for providing secure communications against cyber adversaries. However, there exist many challenges for designing a robust, efficient 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) reconstructing feature representations to make a trade-off between dimensionality reduction 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 traffic type classifications. Extensive experiments reveal the effectiveness of the proposed RTIDS on two publicly available real traffic 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.