CFS-MHA: A Two-Stage Network Intrusion Detection Framework

CFS-MHA: A Two-Stage Network Intrusion Detection Framework

Ritinder Kaur, Neha Gupta
Copyright: © 2022 |Pages: 27
DOI: 10.4018/IJISP.313663
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Abstract

With the increasing modernism in our society, networked computers are playing a pivotal role in dispersion of knowledge, and the protection of critical data in information systems has become a challenge for the research and industrial community. The intrusion detection systems undermine huge amounts of attack data to extrapolate patterns using machine learning techniques. In this paper, a two-stage intrusion detection model has been proposed to employ a blend of diverse attribute selection techniques and machine learning algorithms to provide high performance intrusion detection. The first stage extracts the relevant attributes by applying a hybrid meta-heuristic feature selection algorithm, and in the second stage, supervised machine learning algorithms have been implemented to improve the detection accuracy, execution time, and error rate. NSL-KDD dataset has been used, and the performance of CFS-MHA has been evaluated using different classification strategies. By using 10 attributes and random tree ensemble techniques, CFS-MHA has achieved an accuracy of 81.2% in detection of attacks.
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Attempts have been made in the literature to detect intrusions using Machine learning techniques. The present paper discusses various feature selection techniques and generic machine learning models used in IDS along with their evaluation metrics.

Table 1 enumerates IDS based research papers of past two years (2019-20) that are using ML models to illustrate the use of feature selection techniques, ML models and Evaluation Metrics.

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    It summarizes that NSL-KDD is a prevalent dataset used for studying Intrusion detection.

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    It highlights the most common feature selection shown in Figure 1 and machine learning techniques used by researchers on the NSL-KDD dataset shown in Figure 2.

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