• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Developing an Intrusion Detection Framework for High-Speed Big Data Networks: A Comprehensive Approach


Abstract

In network intrusion detection research, two characteristics are generally considered vital to building efficient intrusion detection systems (IDSs): an optimal feature selection technique and robust classification schemes. However, the emergence of sophisticated network attacks and the advent of big data concepts in intrusion detection domains require two more significant aspects to be addressed: employing an appropriate big data computing framework and utilizing a contemporary dataset to deal with ongoing advancements. As such, we present a comprehensive approach to building an efficient IDS with the aim of strengthening academic anomaly detection research in real-world operational environments. The proposed system has the following four characteristics: (i) it performs optimal feature selection using information gain and branch-and-bound algorithms; (ii) it employs machine learning techniques for classification, namely, Logistic Regression, Naïve Bayes, and Random Forest; (iii) it introduces bulk synchronous parallel processing to handle the computational requirements of large-scale networks; and (iv) it utilizes a real-time contemporary dataset generated by the Information Security Centre of Excellence at the University of Brunswick (ISCX-UNB) to validate its efficacy. Experimental analysis shows the effectiveness of the proposed framework, which is able to achieve high accuracy, low computational cost, and reduced false alarms.


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Cite this article

[IEEE Style]
K. Siddique, Z. Akhtar, M. A. Khan, Y. Jung and Y. Kim, "Developing an Intrusion Detection Framework for High-Speed Big Data Networks: A Comprehensive Approach," KSII Transactions on Internet and Information Systems, vol. 12, no. 8, pp. 4021-4037, 2018. DOI: 10.3837/tiis.2018.08.026.

[ACM Style]
Kamran Siddique, Zahid Akhtar, Muhammad Ashfaq Khan, Yong-Hwan Jung, and Yangwoo Kim. 2018. Developing an Intrusion Detection Framework for High-Speed Big Data Networks: A Comprehensive Approach. KSII Transactions on Internet and Information Systems, 12, 8, (2018), 4021-4037. DOI: 10.3837/tiis.2018.08.026.