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End-to-End adversarial learning for intrusion detection in computer networks

Mohammadi, B ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1109/LCN44214.2019.8990759
  3. Publisher: IEEE Computer Society , 2019
  4. Abstract:
  5. This paper presents a simple yet efficient method for an anomaly-based Intrusion Detection System (IDS). In reality, IDSs can be defined as a one-class classification system, where the normal traffic is the target class. The high diversity of network attacks in addition to the need for generalization, motivate us to propose a semi-supervised method. Inspired by the successes of Generative Adversarial Networks (GANs) for training deep models in semi-unsupervised setting, we have proposed an end-to-end deep architecture for IDS. The proposed architecture is composed of two deep networks, each of which trained by competing with each other to understand the underlying concept of the normal traffic class. The key idea of this paper is to compensate the lack of anomalous traffic by approximately obtain them from normal flows. In this case, our method is not biased towards the available intrusions in the training set leading to more accurate detection. The proposed method has been evaluated on NSL-KDD dataset. The results confirm that our method outperforms the other state-of-the-art approaches. © 2019 IEEE
  6. Keywords:
  7. Computer crime ; Network architecture ; Network security ; Supervised learning ; Adversarial learning ; Adversarial networks ; Anomaly based intrusion detection systems ; Deep architectures ; One-class Classification ; Proposed architectures ; Semi-supervised method ; State-of-the-art approach ; Intrusion detection
  8. Source: 44th Annual IEEE Conference on Local Computer Networks, LCN 2019, 14 October 2019 through 17 October 2019 ; Volume 2019-October , 2019 , Pages 270-273 ; 9781728110288 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/8990759/authors#authors