Malicious URL Detection using Machine Learning: A Survey

By Doyen Sahoo, Chenghao Liu, Steven C. H. Hoi. arXiv.org, 2017

405 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/51006f395255a3c5bed1f418a1b838b2f24b7b38

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Huan Liu
2. Jundong Li
3. V. Varadharajan
4. Steven C. H. Hoi
5. Suhuai Luo
6. Quang Pham
7. Hung Le
8. Min Xu

In Information Systems

1. Xia Hu
2. Hung Le
3. Huan Liu
4. Jundong Li
5. V. Varadharajan
6. Steven C. H. Hoi
7. Quang Pham
8. Doyen Sahoo

In Signal Processing

1. Hung Le
2. Doyen Sahoo
3. Steven C. H. Hoi
4. V. Varadharajan
5. Jundong Li
6. Quang Pham
7. Xia Hu
8. Huan Liu

Citations in the corpus

• Machine Learning and Deep Learning Methods for Cybersecurity (2018, methodology): “[8] present the formal formulation of Malicious URL Detection as a machine-learning task and”
• URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection (2018, methodology): “For a comprehensive review on Malicious URL Detection using Machine Learning, see [33].”
• A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, background): “…role in numerous applications of the cyber security for early detection and prediction of different attacks such as spam classification [29]–[32], fraud detection [33]–[36], malware detection [37]–[40], phishing [41]–[43], darkweb or deep-web sites [44], [45], and intrusion detection [46]–[49].”
• Radar: Residual Analysis for Anomaly Detection in Attributed Networks (2017, background): “, 2017], malicious URL detection [Sahoo et al., 2017] and rare category detection [Zhou et al.”