Efficient BiSRU Combined With Feature Dimensionality Reduction for Abnormal Traffic Detection
Pengpeng Ding, Jinguo Li, Mi Wen, Liangliang Wang, Hongjiao Li
Abstract
Abnormal traffic detection is an important network security technology to protect computer systems from malicious attacks. Existing detection methods are usually based on traditional machine learning, such as Support Vector Machine (SVM), Naive Bayes, etc. They rely heavily on manual design of traffic features and usually shallow feature learning, which get a low accuracy for high-dimensional traffic. Although the method based on Long Short-Term Memory (LSTM) has an excellent ability to detect abnormal traffic. The sequence-dependent structure of LSTM cannot realize parallel computation, which leads to slow model training and limits its applicability. To address the above problem, we propose an efficient Bidirectional Simple Recurrent Unit (BiSRU) combined with feature dimensionality reduction for abnormal traffic detection. Specifically, in order to perform feature dimensionality reduction on the original high-dimensional network traffic, we design a stack Sparse Autoencoder (sSAE) to extract the compressed high-level features. For the purpose of realizing efficient parallel computation and accurate feature extraction, a BiSRU is utilized to extract the bidirectional structural features of the traffic. Finally, the experimental results show that our proposed method significantly outperforms existing methods in terms of accuracy and training time. The method we propose can timely and accurately detect various abnormal traffic and achieve effective network security protection.