Detection of Malicious URLs using Machine Learning Techniques

Naveen, Immadisetti Naga Venkata Durga and Manamohana, K and Rohith, Verma (2019) Detection of Malicious URLs using Machine Learning Techniques. International Journal of Innovative Technology and Exploring Engineering, 8 (4S2). pp. 389-393. ISSN 2278-3075

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The primitive usage of URL (Uniform Resource Locator) is to use as a Web Address. However, some URLs can also be used to host unsolicited content that can potentially result in cyber attacks. These URLs are called malicious URLs. The inability of the end user system to detect and remove the malicious URLs can put the legitimate user in vulnerable condition. Furthermore, usage of malicious URLs may lead to illegitimate access to the user data by adversary. The main motive for malicious URL detection is that they provide an attack surface to the adversary. It is vital to counter these activities via some new methodology. In literature, there have been many filtering mechanisms to detect the malicious URLs. Some of them are Black-Listing, Heuristic Classification etc. These traditional mechanisms rely on keyword matching and URL syntax matching. Therefore, these conventional mechanisms cannot effectively deal with the ever evolving technologies and webaccess techniques. Furthermore, these approaches also fall short in detecting the modern URLs such as short URLs, dark web URLs. In this paper, we propose a novel classification method to address the challenges faced by the traditional mechanisms in malicious URL detection. The proposed classification model is built on sophisticated machine learning methods that not only takes care about the syntactical nature of the URL, but also the semantic and lexical meaning of these dynamically changing URLs. The proposed approach is expected to outperform the existing techniques.

Item Type: Article
Uncontrolled Keywords: Malicious URLs, Black-Listing, machine learning, URL Features, Cyber Crime
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 06 May 2020 04:17
Last Modified: 06 May 2020 04:17

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