Log-based Intrusion Detection System using Machine Learning

Francis Javior, Sonia (2024) Log-based Intrusion Detection System using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

The increasing proportion of cyber-attacks has demanded the development of more secure and effective Intrusion Detection Systems (IDSs). The traditional intrusions was more focused on the network layer, but attack penetration was in-depth in the application layer as well. Intrusion detection systems (IDS) are critical when safeguarding the system networks against malicious threats. As the attackers have learned the traditional accessing method the need for developing complex intrusion systems is so necessary. The traditional Intrusion Detection System was more focused on the network layer, but attack penetration was in-depth in the application layer. To solve this gap, an intrusion-based detection system with the help of log files is designed for detecting web attacks. Log file plays a crucial part in this paper since it records the errors and intrusions that happen in the system. This paper discusses the selection of the significant features that are intended to classify the attack. Managing the log files and selecting the significant features in the data are intended to classify the attack. Multiple datasets are generated and it is preprocessed and trained to learn the complex attack patterns of the network system. With the support of the information gained through the generation of logs, the system identifies the vulnerable activities both in the network and application layer. In this research, nine different types of web attacks are detected using supervised machine learning algorithms. The evaluation of these algorithms has been done by considering the precision, recall, F1-Score and Accuracy factors.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Haque, Rejwanul UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence 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 Artificial Intelligence
Depositing User:	Tamara Malone
Date Deposited:	03 Apr 2025 18:12
Last Modified:	03 Apr 2025 18:12
URI:	https://norma.ncirl.ie/id/eprint/7363

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