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Machine learning (ML), especially deep learning (DL) techniques have been increasingly used in anomaly-based network intrusion detection systems (NIDS).
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T.-H. Cheng, Y.-D. Lin, Y.-C. Lai, and P.-C. Lin, “Evasion Techniques: Sneaking through Your Intrusion Detection/Prevention Systems,” IEEE Communications Surveys & Tutorials
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2018
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S. Gulghane, V. Shingate, S. Bondgulwar, G. Awari, and P. Sagar, “A survey on intrusion detection system using machine learning algorithms,” in International Conference on Innovative Data Communication Technologies and Application
2019
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S. Li, A. Neupane, S. Paul, C. Song, S. V. Krishnamurthy, A. K. R. Chowdhury, and A. Swami, “Stealthy Adversarial Perturbations Against Real-Time Video Classification Systems,” in Network and Distributed System Security Symposium (NDSS)
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P. Laskov and others, “Practical evasion of a learning-based classifier: A case study,” in IEEE Symposium on Security and Privacy (S&P)
2014
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems (NIPS)
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I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and Harnessing Adversarial Examples,” in International Conference on Learning Representations (ICLR)
2015
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W. Xu, Y. Qi, and D. Evans, “Automatically evading classifiers,” in Network and Distributed System Security Symposium (NDSS)
2016
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2016
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2019
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J. Li, S. Ji, T. Du, B. Li, and T. Wang, “TextBugger: Generating Adversarial Text Against Real-world Applications,” in Network and Distributed System Security Symposium (NDSS)
2019
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M. J. Hashemi, G. Cusack, and E. Keller, “Towards evaluation of nidss in adversarial setting,” in Proceedings of the 3rd ACM CoNEXT Workshop on Big DAta, Machine Learning and Artificial Intelligence for Data Communication Networks
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X. Peng, W. Huang, and Z. Shi, “Adversarial attack against dos intrusion detection: An improved boundary-based method,” in 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)
2019
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G. Apruzzese, M. Colajanni, and M. Marchetti, “Evaluating the effectiveness of adversarial attacks against botnet detectors,” in 2019 IEEE 18th International Symposium on Network Computing and Applications (NCA)
2019
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Y. Zhong, W. Chen, Z. Wang, Y. Chen, K. Wang, Y. Li, X. Yin, X. Shi, J. Yang, and K. Li, “Helad: A novel network anomaly detection model based on heterogeneous ensemble learning,” Computer Networks
2020
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R. Doriguzzi-Corin, S. Millar, S. Scott-Hayward, J. Martinez-del Rincon, and D. Siracusa, “Lucid: A practical, lightweight deep learning solution for ddos attack detection,” IEEE Transactions on Network and Service Management
2020
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C. Xu, J. Shen, and X. Du, “A method of few-shot network intrusion detection based on meta-learning framework,” IEEE Transactions on Information Forensics and Security
2020
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M. A. Ferrag, L. Maglaras, S. Moschoyiannis, and H. Janicke, “Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study,” Journal of Information Security and Applications
2020
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Y. Zang, F. Qi, C. Yang, Z. Liu, M. Zhang, Q. Liu, and M. Sun, “Word-level textual adversarial attacking as combinatorial optimization,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
2020
Closest in time.
Y. Wang, Y.-a. Tan, W. Zhang, Y. Zhao, and X. Kuang, “An adversarial attack on dnn-based black-box object detectors,” Journal of Network and Computer Applications
2020
Closest in time.