DESIGN AND IMPLEMENTATION OF A PREDICTIVE MODEL OF NETWORK INTRUSION DETECTION SYSTEMS USING MACHINE LEARNING APPROACH

DESIGN AND IMPLEMENTATION OF A PREDICTIVE MODEL OF NETWORK INTRUSION DETECTION SYSTEMS USING MACHINE LEARNING APPROACH

ABSTRACT

Information and network security issues are very critical in this Era. Information is playing a vital role to realize informed and civilized society and to create democratic, transparent and accountable government, and to assure sustainable economic development. On the other hand, the reliance on information systems is increasing the vulnerability of organizations for cyber-attacks which are becoming highly complicated, dynamic and destructive. In order to protect organizations from cyber-attacks and minimize their impact, it is essential to ensure the security of information and information systems. Machine learning techniques provide a promising result in improving Detection accuracy of intrusion detection system (IDS). A variety of machine learning techniques have been designed and integrated with IDSs. But Most of the Intrusion detection systems still have poor intrusion detection rate and high false positive rate. This thesis focused on ensemble method involving the integration of predictions by multiple individual classifiers. Ensemble method enable to compensate for the weakness of individual classifiers and use their combined knowledge to enhance its performance, different ensemble methods in the field are analyzed, taking into consideration different types of ensembles and various approaches for integrating the predictions of individual classifiers for an ensemble classifier. This research has attempted to build a predictive ensemble ML model for intrusion detection using a new standard dataset from the Canadian Institute for Cyber security intrusion detection system (CIC-IDS2017) for performance evaluation. Simulation outcomes prove that the proposed ensemble model outperforms current IDS systems, attaining accuracy of up to 99%. The performance of this algorithm is measured using accuracy, precision, false positive, F1 score, and recall which found promising results for deployment on real network infrastructure.

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