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Applying Machine learning and Deep Learning Techniques for Improvement in Network Intrusion Detection System

Londhe, Chaitanya Anand (2021) Applying Machine learning and Deep Learning Techniques for Improvement in Network Intrusion Detection System. Masters thesis, Dublin, National College of Ireland.

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Abstract

The quick progress in the web and networking domains has taken place by means of a huge growth of the network size and data. Moreover, attackers might be spotted within the network with the intention of initiating numerous attacks. The Intrusion Detection System (IDS) is a system that monitors network traffic and ensures integrity, confidentiality, and availability from network intrusion. Even despite the greatest efforts of academics, IDS faces problems till date in developing identification accuracy while minimizing false alarms and detecting new attacks. Machine learning (ML) and Deep Learning (DL) based IDS software were recently deployed as viable techniques of detecting network breaches rapidly. The purpose of this study is primarily focused on the prominent ML and DL techniques utilized in the modeling of Network-based IDS (NIDS) architectures. The recommended technique, performance measures, data gathering and recent breakthroughs in ML and in the DL of Network Intrusion Detection System (NIDS) are reviewed. Many research problems have been identified, and possible research opportunities have emerged with the limits of the existing technique in creating ML and DL based NIDS.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Network Intrusion Detection System; Machine Learning; Deep Learning
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Clara Chan
Date Deposited: 01 Nov 2021 11:43
Last Modified: 01 Nov 2021 11:43
URI: https://norma.ncirl.ie/id/eprint/5119

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