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Ong Vienna Lee
University Malaysia Pahang

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A malicious URLs detection system using optimization and machine learning classifiers Ong Vienna Lee; Ahmad Heryanto; Mohd Faizal Ab Razak; Anis Farihan Mat Raffei; Danakorn Nincarean Eh Phon; Shahreen Kasim; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1210-1214

Abstract

The openness of the World Wide Web (Web) has become more exposed to cyber-attacks. An attacker performs the cyber-attacks on Web using malware Uniform Resource Locators (URLs) since it widely used by internet users. Therefore, a significant approach is required to detect malicious URLs and identify their nature attack. This study aims to assess the efficiency of the machine learning approach to detect and identify malicious URLs. In this study, we applied features optimization approaches by using a bio-inspired algorithm for selecting significant URL features which able to detect malicious URLs applications. By using machine learning approach with static analysis technique is used for detecting malicious URLs applications. Based on this combination as well as significant features, this paper shows promising results with higher detection accuracy.  The bio-inspired algorithm: particle swarm optimization (PSO) is used to optimized URLs features. In detecting malicious URLs, it shows that naïve Bayes and support vector machine (SVM) are able to achieve high detection accuracy with rate value of 99%, using URL as a feature.
Co-Authors Ahmad Heryanto Anis Farihan Mat Raffei Danakorn Nincarean Eh Phon Mohd Faizal Ab Razak Shahreen Kasim Tole Sutikno