Finding effective classifier for malicious URL detection-Reference-Cited by-同舟云学术

Finding effective classifier for malicious URL detection

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2018 2nd International Conference on Management Engineering, Software Engineering and Service Sciences - ICMSS 2018
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Author:

Liu Chunlin¹,Wang Lidong²,Lang Bo¹,Zhou Yuan²

Affiliation:

1. State Key Lab of Software Development Environment School of Computer Science and Engineering, Beihang University, Beijing, China

2. National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, China

Publisher

ACM Press

Reference13 articles.

1. Choi, H., Zhu, B.B., Lee,H.: Detecting malicious web links and identifying their attack types. In: Fox, A. (ed.) 2nd USENIX Conference on Web Application Development, WebApps'11, Portland, Oregon, USA, June 15-16, 2011. USENIX Association(2011)

2. Ma, J., Saul, L.K., Savage, S., Voelker, G.M.: Beyond blacklists: learning to detect malicious web sites from suspicious urls. In: IV, J.F.E., Fogelman-Souli_e, F., Flach, P.A., Zaki, M.J. (eds.) Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 1245--1254. ACM (2009)

3. Zhang Y, Hong J I, Cranor L F. Cantina: a content-based approach to detecting phishing web sites [C] //16th International World Wide Web Conference. Banff, Alberta, Canada, 2007: 639--648.

4. Fu A Y, Liu W Y, Deng X T. Detecting phishing web pages with visual similarity assessment based on earth mover's distance (EMD) [J] IEEE Transactions on Dependable and Secure Computing, 2006,3 (4): 301--311.

5. Sujata Garera, Niels Provos, Monica Chew,et a1..A Framework for Detection and Measurement of Phishing Attacks[C]. Proceedings of 2007 ACM Workshop on Recurring Mal-code. Alexandria, VA, USA,2007:1--8

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