Mobile Networks and Management. 9th International Conference, MONAMI 2017, Melbourne, Australia, December 13-15, 2017, Proceedings

Research Article

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

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  • @INPROCEEDINGS{10.1007/978-3-319-90775-8_10,
        author={Nam Tran and Ruhul Sarker and Jiankun Hu},
        title={An Approach for Host-Based Intrusion Detection System Design Using Convolutional Neural Network},
        proceedings={Mobile Networks and Management. 9th International Conference, MONAMI 2017, Melbourne, Australia, December 13-15, 2017, Proceedings},
        proceedings_a={MONAMI},
        year={2018},
        month={5},
        keywords={Intrusion Detection System Host-Based Convolutional Neural Network},
        doi={10.1007/978-3-319-90775-8_10}
    }
    
  • Nam Tran
    Ruhul Sarker
    Jiankun Hu
    Year: 2018
    An Approach for Host-Based Intrusion Detection System Design Using Convolutional Neural Network
    MONAMI
    Springer
    DOI: 10.1007/978-3-319-90775-8_10
Nam Tran1,*, Ruhul Sarker1,*, Jiankun Hu1,*
  • 1: University of New South Wales Canberra at the Australian Defence Force Academy
*Contact email: nam.tran@student.adfa.edu.au, r.sarker@adfa.edu.au, j.hu@adfa.edu.au

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.