An intelligent approach to automated OS log analysis for enhanced security

Johnphill, O ORCID: https://orcid.org/0000-0001-8373-0727, Sadiq, AS ORCID: https://orcid.org/0000-0002-5746-0257, Kaiwartya, O ORCID: https://orcid.org/0000-0001-9669-8244 and Aljaidi, M, 2024. An intelligent approach to automated OS log analysis for enhanced security. Information, 15 (10): 657. ISSN 2078-2489

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Abstract

Self-healing systems have become essential in modern computing for ensuring continuous and secure operations while minimising downtime and maintenance costs. These systems autonomously detect, diagnose, and correct anomalies, with effective self-healing relying on accurate interpretation of system logs generated by operating systems (OS). Manual analysis of these logs in complex environments is often cumbersome, time-consuming, and error-prone, highlighting the need for automated, reliable log analysis methods. Our research introduces an intelligent methodology for creating self-healing systems for multiple OS, focusing on log classification using CountVectorizer and the Multinomial Naive Bayes algorithm. This approach involves preprocessing OS logs to ensure quality, converting them into a numerical format with CountVectorizer, and then classifying them using the Naive Bayes algorithm. The system classifies multiple OS logs into distinct categories, identifying errors and warnings. We tested our model on logs from four major OS; Mac, Android, Linux, and Windows; sourced from Zenodo to simulate real-world scenarios. The model's accuracy, precision, and reliability were evaluated, demonstrating its potential for deployment in practical self-healing systems.

Item Type:	Journal article
Publication Title:	Information
Creators:	Johnphill, O., Sadiq, A.S., Kaiwartya, O. and Aljaidi, M.
Publisher:	MDPI
Date:	19 October 2024
Volume:	15
Number:	10
ISSN:	2078-2489
Identifiers:	Number Type 10.3390/info15100657 DOI 2254900 Other
Rights:	© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	16 Oct 2024 14:30
Last Modified:	22 Oct 2024 07:47
URI:	https://irep.ntu.ac.uk/id/eprint/52431

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