Application of deep learning model in network reconnaissance attack detection

Authors

  • Nguyễn Thị Dung
  • Nguyễn Văn Quân
  • Nguyễn Việt Hùng

DOI:

https://doi.org/10.54654/isj.v1i16.922

Keywords:

network reconnaissance, anomaly detection, intrusion detection system, machine learning, deep learning

Tóm tắt

Abstract— In recent years, the number of new types of attacks has increased dramatically. Although there are many types of attack techniques, all of them are following the similar chain of attack, beginning with network reconnaissance phase. Therefore, network reconnaissance attack detection problem is important for every Intrusion Detection System (IDS). In fact, network intrusion detection systems are based on pre-defined rules so they are not able to detect new attacks or variants of known attacks. Meanwhile, hackers have developed many automated toolkits that allow subtle changes to the attack  behavior sufficient for IDS to treat as a zero-day attack. To overcome this limitation, many machine learning models have been applied in IDS and implemented in a real network. In this paper, we propose a new approach that uses two stage AutoEncoder to detect network reconnaissance attacks. The proposed approach is evaluated on network security datasets: NSL-KDD, UNSW_NB15, four scenarios of the CTU13 datasets and compared to existing methods.

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Published

2023-02-13

How to Cite

Dung, N. T., Quân, N. V., & Hùng, N. V. (2023). Application of deep learning model in network reconnaissance attack detection. Journal of Science and Technology on Information Security, 2(16), 60-72. https://doi.org/10.54654/isj.v1i16.922

Issue

No 2. CS (16) 2022

Section

Papers

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3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).