Approach to Improving Machine Learning Models for Intrusion Detection System

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dc.contributor.authorLabib, Ahmad Ibtisam
dc.contributor.authorChy, Shamsuddin Ahmmed
dc.contributor.authorHossain, Md. Shahriar
dc.date.accessioned2024-03-30T08:03:02Z
dc.date.available2024-03-30T08:03:02Z
dc.date.issued2022-07
dc.descriptionBachelor of Science (B.Sc.) in Computer Science and Engineering (CSE) Spring-2022en_US
dc.description.abstractIn today's digital world, there are several security risks that digital assets must contend with. Systems for detecting intrusions (IDS) are essential security tools that protect digital assets. But their usefulness depends on meeting strict accuracy requirements, and their effectiveness depends on timely alarms. This study offers a novel IDS model that combines deep learning and machine learning methods as a solution to these problems. The study applies several classification techniques, such as Gaussian Naive Bayes (GNB), Random Forest (RF), Decision Tree, K-Nearest Neighbors (KNN), Soft Voting, and Hard Voting, using the well known KDD Cup-1999 dataset. After a large-scale dataset was processed, the Decision Tree method performed better than the others, with a 99.9% accuracy rate. This study aims to investigate the effects of soft voting and hard voting, a novel application in IDS. Decision Tree proved to be the better performance in spite of these efforts. By offering information about algorithmic efficacy, the research advances the field of intrusion detection and helps decision-makers in the design and deployment of intrusion detection systems. These findings have implications for improving digital asset protection against changing cyber threats.en_US
dc.identifier.urihttp://dspace.iiuc.ac.bd:8080/xmlui/handle/123456789/8146
dc.language.isoenen_US
dc.publisherInternational Islamic University Chittagongen_US
dc.subjectMachine learningen_US
dc.subjectIDSen_US
dc.subjectKDD Cupen_US
dc.subjectSecurityen_US
dc.subjectDTen_US
dc.subjectGNBen_US
dc.subjectRFen_US
dc.subjectKNNen_US
dc.subjectEnsembleen_US
dc.titleApproach to Improving Machine Learning Models for Intrusion Detection Systemen_US
dc.typeThesisen_US

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