Version 1
: Received: 10 October 2024 / Approved: 11 October 2024 / Online: 14 October 2024 (16:31:46 CEST)
How to cite:
Johnphill, O.; Safaa Sadiq, A.; Kaiwartya, O.; Aljaidi, M. An Intelligent Approach to Automated OS Log Analysis for Enhanced Security. Preprints2024, 2024100951. https://doi.org/10.20944/preprints202410.0951.v1
Johnphill, O.; Safaa Sadiq, A.; Kaiwartya, O.; Aljaidi, M. An Intelligent Approach to Automated OS Log Analysis for Enhanced Security. Preprints 2024, 2024100951. https://doi.org/10.20944/preprints202410.0951.v1
Johnphill, O.; Safaa Sadiq, A.; Kaiwartya, O.; Aljaidi, M. An Intelligent Approach to Automated OS Log Analysis for Enhanced Security. Preprints2024, 2024100951. https://doi.org/10.20944/preprints202410.0951.v1
APA Style
Johnphill, O., Safaa Sadiq, A., Kaiwartya, O., & Aljaidi, M. (2024). An Intelligent Approach to Automated OS Log Analysis for Enhanced Security. Preprints. https://doi.org/10.20944/preprints202410.0951.v1
Chicago/Turabian Style
Johnphill, O., Omprakash Kaiwartya and Mohammad Aljaidi. 2024 "An Intelligent Approach to Automated OS Log Analysis for Enhanced Security" Preprints. https://doi.org/10.20944/preprints202410.0951.v1
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.
Keywords
Multi-class system log classification; Operating system log mining; Self-healing systems; Cybersecurity; Countvectorizer; Feature selection; Artificial Intelligence
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
