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 plays a vital role in realizing an informed and civilized society, creating a democratic, transparent, and accountable government, and ensuring sustainable economic development. On the other hand, the reliance on information systems is increasing the vulnerability of organizations to cyberattacks, which are becoming highly complicated, dynamic, and destructive. In order to protect organizations from cyberattacks 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 the detection accuracy of intrusion detection systems (IDS). A variety of machine learning techniques have been designed and integrated with IDSs. However most intrusion detection systems still have poor intrusion detection rates and high false-positive rates. This thesis focused on the ensemble method, which involves the integration of predictions by multiple individual classifiers. The ensemble method enables them to compensate for the weaknesses of individual classifiers and use their combined knowledge to enhance their performance. Different ensemble methods in the field are analysed, 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 an 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. what is ips

DESIGN AND IMPLEMENTATION OF A PREDICTIVE MODEL OF NETWORK INTRUSION DETECTION SYSTEMS USING A MACHINE LEARNING APPROACH,GET MORE COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS

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