A scalable network intrusion detection system towards detecting, discovering, and learning unknown attacks

Network intrusion detection systems (IDSs) based on deep learning have reached fairly accurate attack detection rates. But these deep learning approaches usually have been performed in a closed-set protocol that only known classes appear in training are considered during classification, the existing IDSs will fail to detect the unknown attacks and misclassify them as the training known classes, hence are not scalable. Furthermore, these IDSs are not efficient for updating the deep detection model once new attacks are discovered. To address those problems, we propose a scalable IDS towards detecting, discovering, and learning unknown attacks, it has three components. Firstly, we propose the open-set classification network (OCN) to detect unknown attacks, OCN based on the convolutional neural network adopts the nearest class mean (NCM) classifier, two new loss are designed to jointly optimize it, including Fisher loss and maximum mean discrepancy (MMD) loss. Subsequently, the semantic embedding clustering method is proposed to discover the hidden unknown attacks from all unknown instances detected by OCN. Then we propose the incremental nearest cluster centroid (INCC) method for learning the discovered unknown attacks through updating the NCM classifier. Extensive experiments on KDDCUP’99 dataset and CICIDS2017 dataset indicate that our OCN outperforms the state-of-the-art comparison methods in detecting multiple types of unknown attacks. Our experiments also verify the feasibility of the semantic embedding clustering method and INCC in discovering and learning unknown attacks.