Publication Date: 2023/01/07
Abstract: The risk of network information insecurity is growing rapidly in number and level of risk is very high. The methods mostly used by hackers today is to attack whole system and exploit human vulnerabilities. These techniques include social engineering, phishing, pharming, etc. One of the steps in conducting these attacks is to deceive users with fake Uniform Resource Locators (URLs). As a result, fake URL detection is of great interest nowadays. There have been several scientific studies showing a number of methods to detect malicious URLs based on machine learning and deep learning techniques. In this paper, we propose a Fake URL detection method using machine learning techniques based on our proposed URL behaviours and attributes. Moreover, bigdata technology is also exploited to improve the capability of detection malicious URLs based on abnormal behaviours. In short, the proposed detection system consists of a new set of URLs features and behaviours, a machine learning algorithm, and a bigdata technology. The experimental results show that the proposed URL attributes and behaviour can help improve the ability to detect malicious URL significantly. This is suggested that the proposed system may be considered as an optimized and friendly used solution for malicious URL detection.
Keywords: URL; Malicious URL Detection; Phishing; Machine Learning
DOI: https://doi.org/10.5281/zenodo.7511267
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT22DEC1128.pdf
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