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An Approach for Host-Based Intrusion Detection System Design Using Convolutional Neural Network

  • Nam Nhat TranEmail author
  • Ruhul Sarker
  • Jiankun Hu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 235)

Abstract

Along with the drastic growth of telecommunication and networking, the cyber-threats are getting more and more sophisticated and certainly leading to severe consequences. With the fact that various segments of industrial systems are deployed with Information and Computer Technology, the damage of cyber-attacks is now expanding to physical infrastructure. In order to mitigate the damage as well as reduce the False Alarm Rate, an advanced yet well-design Intrusion Detection System (IDS) must be deployed. This paper focuses on system call traces as an object for designing a Host-based anomaly IDS. Sharing several similarities with research objects in Natural Language Processing and Image Recognition, a Host-based IDS design procedure based on Convolutional Neural Network (CNN) for system call traces is implemented. The decent preliminary results harvested from modern benchmarking datasets NGIDS-DS and ADFA-LD demonstrated this approachs feasibility.

Keywords

Intrusion Detection System Host-Based Convolutional Neural Network 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.University of New South Wales Canberra at the Australian Defence Force AcademyCanberraAustralia

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