Lexical features based malicious URL detection using machine learning techniques. (2021)

• Record Type: Journal Article
• Authors: Saleem Raja, A.; Vinodini, R.; Kavitha, A.
• Is Part Of: Materials today. Volume 47:Part 1(2021)
• Page Start: 163
• Page End: 166
• Publication Date: 2021
• Subjects: Malicious URL detection -- Machine learning -- Feature extraction -- Feature reduction; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.04.041
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Most sophisticated cyber-attack technique used by the cyber criminals is creating and spreading malicious domain names or malicious URLs through email, messages, popups etc. Malicious URL are the web pages targeted towards the internet user to spread the malware, virus, warms etc once the user visited. Main intension of the attack is to steal the victim information, user credentials or install the malware in the victim's system. So, it is necessary to adapt the system which should detect the malicious URLs and prevent from the attack. Researchers suggest numerous methods but machine learning based detection method performs better then methods. This paper presents the light weighted method which includes only lexical features of the URL. The result shows the Random Forest classifier performs better than the other classifiers in terms of accuracy.