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Publication Type : Conference Paper
Publisher : Communications in Computer and Information Science
Source : Communications in Computer and Information Science (2020)
ISBN : 9789811548246
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2020
Abstract : Network Intrusion Detection System (NIDS) is a method that is utilized to categorize network traffic as malicious or normal. Anomaly-based method and signature-based method are the traditional approaches used for network intrusion detection. The signature-based approach can only detect familiar attacks whereas the anomaly-based approach shows promising results in detecting new unknown attacks. Machine Learning (ML) based approaches have been studied in the past for anomaly-based NIDS. In recent years, the Deep Learning (DL) algorithms have been widely utilized for intrusion detection due to its capability to obtain optimal feature representation automatically. Even though DL based approaches improves the accuracy of the detection tremendously, they are prone to adversarial attacks. The attackers can trick the model to wrongly classify the adversarial samples into a particular target class. In this paper, the performance analysis of several ML and DL models are carried out for intrusion detection in both adversarial and non-adversarial environment. The models are trained on the NSLKDD dataset which contains a total of 148,517 data points. The robustness of several models against adversarial samples is studied.
Cite this Research Publication : S. Srinivasan, Ketha, S., Ravi, V., Soman, A., and Dr. Soman K. P., “Towards Evaluating the Robustness of Deep Intrusion Detection Models in Adversarial Environment”, in Communications in Computer and Information Science, 2020.