RFG-HELAD: A Robust Fine-Grained Network Traffic Anomaly Detection Model Based on Heterogeneous Ensemble Learning
Ying Zhong, Zhiliang Wang, Xingang Shi, Jiahai Yang, Keqin Li · IEEE Transactions on Information Forensics and Security · 2024
Fine-grained attack detection is an important network security task. A large number of machine learning/deep learning( ML/DL) based algorithms have been proposed. However, attacks not present in the training set pose a challenge to the model (openset problem). Further, ML/DL based models face the problem of adversarial attacks. Despite the large amount of work attempting to address these problems, there are still some challenges as follows. First, the open-set problem in fine-grained attack detection is difficult to solve because there is no effective representation of the distribution of unknown attacks. Second, in the open set environment, how the fine-grained attack detection model resists the adversarial attack is a more difficult problem. For example, the presence of unknown attacks poses a challenge for adversarial defense. For these reasons, we propose the RFG-HELAD model, which consists of aKclassification model based on deep neural network (DNN) with contrastive learning (CL), and aK+ 1 classification model combining a generative adversarial networks (GAN) with two discriminators and deepk-nearest neighbors (Deep kNN). Among them, Deep kNN uses latent features from GAN and contrastive learning as input, which is essentially a distance-based out-of-distribution detection algorithm used to determine unknown attacks. The large category of unknown attacks has been added to theKclassification, so it is aK+ 1 classification. To further improve the robustness of the RFG-HELAD model, we perform Fourier transform as well as feature fusion on the features, and also conduct adversarial training on theKclassification model. Generative adversarial training of our GAN model can implicitly defend against adversarial attack. Experiments show that our model is superior to other state-of-the-art (SOTA) models in the presence of unknown attacks as well as under adversarial attacks. Especially, our model improves the accuracy by at least 18.7% over the corresponding SOTA model with adversarial defense. Further, we discuss the grounded deployment of the model and demonstrate its feasibility.