NDLI: Malicious URL prediction based on community detection

Content Provider	IEEE Xplore Digital Library
Author	Zheng Li-xiong Xu Xiao-lin Li Jia Zhang Lu Pan Xuan-chen Ma Zhi-yuan Zhang Li-hong
Copyright Year	2015
Description	Author affiliation: Antiy Labs., Wuhan, China (Zhang Lu; Pan Xuan-chen; Ma Zhi-yuan; Zhang Li-hong) \|\| Nat. Comput. Network Emergency Response Tech. Team, Coordination Center of China, Beijing, China (Zheng Li-xiong; Xu Xiao-lin; Li Jia)
Abstract	Traditional Anti-virus technology is primarily based on static analysis and dynamic monitoring. However, both technologies are heavily depended on application files, which increase the risk of being attacked, wasting of time and network bandwidth. In this study, we propose a new graph-based method, through which we can preliminary detect malicious URL without application file. First, the relationship between URLs can be found through the relationship between people and URLs. Then the association rules can be mined with confidence of each frequent URLs. Secondly, the networks of URLs was built through the association rules. When the networks of URLs were finished, we clustered the date with modularity to detect communities and every community represents different types of URLs. We suppose that a URL has association with one community, then the URL is malicious probably. In our experiments, we successfully captured 82 % of malicious samples, getting a higher capture than using traditional methods.
Starting Page	1
Ending Page	7
File Size	882273
Page Count	7
File Format	PDF
e-ISBN	9781467379779
DOI	10.1109/SSIC.2015.7245681
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uniform resource locators Malicious URL Community Detection Mobile communication Anti-Virus Association Rules Malware Association rules Monitoring
Content Type	Text
Resource Type	Article

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