Shuqiang Lu

Dongqi Han, Zhiliang Wang, Ying Zhong et al.

Machine learning (ML), especially deep learning (DL) techniques have been increasingly used in anomaly-based network intrusion detection systems (NIDS). However, ML/DL has shown to be extremely vulnerable to adversarial attacks, especially in such security-sensitive systems. Many adversarial attacks have been proposed to evaluate the robustness of ML-based NIDSs. Unfortunately, existing attacks mostly focused on feature-space and/or white-box attacks, which make impractical assumptions in real-world scenarios, leaving the study on practical gray/black-box attacks largely unexplored. To bridge this gap, we conduct the first systematic study of the gray/black-box traffic-space adversarial attacks to evaluate the robustness of ML-based NIDSs. Our work outperforms previous ones in the following aspects: (i) practical-the proposed attack can automatically mutate original traffic with extremely limited knowledge and affordable overhead while preserving its functionality; (ii) generic-the proposed attack is effective for evaluating the robustness of various NIDSs using diverse ML/DL models and non-payload-based features; (iii) explainable-we propose an explanation method for the fragile robustness of ML-based NIDSs. Based on this, we also propose a defense scheme against adversarial attacks to improve system robustness. We extensively evaluate the robustness of various NIDSs using diverse feature sets and ML/DL models. Experimental results show our attack is effective (e.g., >97% evasion rate in half cases for Kitsune, a state-of-the-art NIDS) with affordable execution cost and the proposed defense method can effectively mitigate such attacks (evasion rate is reduced by >50% in most cases).

Shuqiang Lu, Lingyun Ying, Wenjie Lin et al.

With the development of artificial intelligence algorithms like deep learning models and the successful applications in many different fields, further similar trails of deep learning technology have been made in cyber security area. It shows the preferable performance not only in academic security research but also in industry practices when dealing with part of cyber security issues by deep learning methods compared to those conventional rules. Especially for the malware detection and classification tasks, it saves generous time cost and promotes the accuracy for a total pipeline of malware detection system. In this paper, we construct special deep neural network, ie, MalDeepNet (TB-Malnet and IB-Malnet) for malware dynamic behavior classification tasks. Then we build the family clustering algorithm based on deep learning and fulfil related testing. Except that, we also design a novel malware prediction model which could detect the malware coming in future through the Mal Generative Adversarial Network (Mal-GAN) implementation. All those algorithms present fairly considerable value in related datasets afterwards.