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Preprint / Version 1

Optimal feature selection for firewall log analysis using Machine learning and Hybrid Metaheuristic algorithms

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DOI:

https://doi.org/10.31224/osf.io/pm3hy

Abstract

Firewall log classification is important to monitor network traffic. Most firewall log classification via machine learning has shown good result by network-related features and classifiers. However, feature with many dimensions take a lot of time to do classification. In this paper, we applied a method of feature selection using optimized bee swarm optimization with reinforcement learning. We evaluated average performance by accuracy, macro-averaged precision, macro-averaged recall, and macro-averaged F1 score in 5-stratified folds using a random forest, k-nearest neighbor, and naïve bayes classifier. As a results, it could be applied for an automatic firewall log analysis system.

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