Computer Science > Machine Learning
[Submitted on 25 Jan 2017 (v1), last revised 21 Aug 2019 (this version, v3)]
Title:Malicious URL Detection using Machine Learning: A Survey
View PDFAbstract:Malicious URL, a.k.a. malicious website, is a common and serious threat to cybersecurity. Malicious URLs host unsolicited content (spam, phishing, drive-by exploits, etc.) and lure unsuspecting users to become victims of scams (monetary loss, theft of private information, and malware installation), and cause losses of billions of dollars every year. It is imperative to detect and act on such threats in a timely manner. Traditionally, this detection is done mostly through the usage of blacklists. However, blacklists cannot be exhaustive, and lack the ability to detect newly generated malicious URLs. To improve the generality of malicious URL detectors, machine learning techniques have been explored with increasing attention in recent years. This article aims to provide a comprehensive survey and a structural understanding of Malicious URL Detection techniques using machine learning. We present the formal formulation of Malicious URL Detection as a machine learning task, and categorize and review the contributions of literature studies that addresses different dimensions of this problem (feature representation, algorithm design, etc.). Further, this article provides a timely and comprehensive survey for a range of different audiences, not only for machine learning researchers and engineers in academia, but also for professionals and practitioners in cybersecurity industry, to help them understand the state of the art and facilitate their own research and practical applications. We also discuss practical issues in system design, open research challenges, and point out some important directions for future research.
Submission history
From: Doyen Sahoo [view email][v1] Wed, 25 Jan 2017 06:46:14 UTC (409 KB)
[v2] Thu, 16 Mar 2017 07:12:50 UTC (411 KB)
[v3] Wed, 21 Aug 2019 10:38:24 UTC (291 KB)
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