Machine Learning-Based Comparative Analysis of Intrusion Detection Systems for Linux Networks

Jadhav, Kunal Sanjaykumar (2024) Machine Learning-Based Comparative Analysis of Intrusion Detection Systems for Linux Networks. Masters thesis, Dublin, National College of Ireland.

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Abstract

Linux-based computer systems are popular due to their flexibility, stability, and opensource nature. Due to their popularity, Linux Networks are susceptible to cyberattacks, requiring strong protection. Intrusion detection systems protect Linux Networks. A detailed comparison of Linux network IDS systems using machine learning is done in this research. Machine learning methods like the TensorFlow and Scikit-learn analyse complicated network traffic patterns and anomalies to identify the intrusions, whereas traditional IDS systems like Snort employ predetermined rules and patterns. Realistic network simulations, Metasploitable security assessment, and IDS effectiveness evaluation utilising detection accuracy, false positive rate, and reaction time are part of the study.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Aleburu, Joel UNSPECIFIED
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 Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	30 Jul 2025 09:20
Last Modified:	30 Jul 2025 09:20
URI:	https://norma.ncirl.ie/id/eprint/8321

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