参考文献
[1] Chi-Hsuan Huang, Tsung-Han Lee, Lin-huang Chang,等. Adversarial Attacks on SDN-Based Deep Learning IDS System[C]. International Conference on Mobile & Wireless Technology. Springer, Singapore, 2018.
[2] M. Rigaki. Adversarial Deep Learning Against Intrusion Detection Classifiers. Dissertation, 2017.
[3] Ibitoye O, Shafiq O, Matrawy A, et al. Analyzing Adversarial Attacks against Deep Learning for Intrusion Detection in IoT Networks[C]. global communications conference, 2019.
[4] Clements J, Yang Y, Sharma A A, et al. Rallying Adversarial Techniques against Deep Learning for Network Security.[J]. arXiv: Cryptography and Security, 2019.
[5] Wang Z. Deep Learning-Based Intrusion Detection With Adversaries[J]. IEEE Access, 2018: 38367-38384.
[6] Hartl A, Bachl M, Fabini J, et al. Explainability and Adversarial Robustness for RNNs.[J]. arXiv: Learning, 2019.
[7] 潘一鸣, 林家骏. 基于生成对抗网络的恶意网络流生成及验证[J]. 华东理工大学学报(自然科学版), 2019, 45(02):165-171.
[8] Rigaki M, Garcia S. Bringing a GAN to a Knife-Fight: Adapting Malware Communication to Avoid Detection[C]. ieee symposium on security and privacy, 2018: 70-75.
[9] Usama M, Asim M, Latif S, et al. Generative Adversarial Networks For Launching and Thwarting Adversarial Attacks on Network Intrusion Detection Systems[C]. international conference on wireless communications and mobile computing, 2019: 78-83.
[10] Hashemi, M. J., Cusack, G, et al. Towards Evaluation of NIDSs in Adversarial Setting. the 3rd ACM CoNEXT Workshop on Big Data, Machine Learning and Artificial Intelligence for Data
Communication Networks. 2019: 14-21.
[11] Kuppa A, Grzonkowski S, Asghar M R, et al. Black Box Attacks on Deep Anomaly Detectors[C]. availability, reliability and security, 2019.
[12] Alhajjar, E, Paul M and Nathanie D. B. Adversarial Machine Learning in Network Intrusion Detection Systems. arXiv preprint arXiv:2004.11898 (2020).
[13] Huang W, Peng X, Shi Z, et al. Adversarial Attack against LSTM-based DDoS Intrusion Detection System. arXiv preprint arXiv:2613.1684 (2020).
[14] Peng X, Huang W, Shi Z, et al. Adversarial Attack Against DoS Intrusion Detection: An Improved Boundary-Based Method[C]. international conference on tools with artificial intelligence, 2019.
[15] Goodfellow I J , Shlens J , Szegedy C . Explaining and harnessing adversarial examples[C]// ICML. 2015.
[16] Szegedy, C, et al. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013).
[17] Carlini N , Wagner D . Towards Evaluating the Robustness of Neural Networks[J]. 2016.