A malicious URLs detection system using optimization and machine learning classifiers

Lee, Ong Vienna and Heryanto, Ahmad and Mohd Faizal, Ab Razak and Anis Farihan, Mat Raffei and Eh Phon, Danakorn Nincarean and Shahreen, Kasim and Sutikno, Tole (2020) A malicious URLs detection system using optimization and machine learning classifiers. Indonesian Journal of Electrical Engineering and Computer Science, 17 (3). pp. 1210-1214. ISSN 2502-4752. (Published)

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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.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Android; Detection system; Features optimization; Machine learning; URLs
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division: Faculty of Computing
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 19 Jan 2024 04:00
Last Modified: 19 Jan 2024 04:00
URI: http://umpir.ump.edu.my/id/eprint/40105
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