Twitter Scanner To Search Malicious URLs for Twitter Users

Nilesh B. Nikumbhe, Chhaya Nayak, Vaibhav Joshi


Over the last few years, there is tremendous use of online social networking sites. It’s also providing opportunities for hackers to enter easily in network and do their unauthorized activities. There are many notable social networking websites like Twitter, Facebook and Google+ etc. These are popularly practiced by numerous people to become linked up with each other and partake their daily happenings through it. Here we focus on twitter for an experiment which is more popular for micro-blogging and its community interact through publishing text-based posts of 140 characters known as tweets. By considering this popularity of tweeter hacker’s use of short Uniform Resource Locator (URL), as a result it disseminates viruses on user accounts. Our study is based on examining the malicious content or their short URLs and protect the user from unauthorized activities. We introduce such a system which provides the security to multiple users of twitter. Besides, they get some alert mails. Our goal is to download URLs in real time from multiple accounts. Then we get entry points of correlated URLs. Crawler browser marks the suspicious URL. This system finds such malicious URLs by using five features like initial URL, similar text, friend follower ratio and relative URLs. Then alert mail is sent to users, which is added to the host.

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Twitter Developers, “Streaming API,” https:// docs/streaming-api.

J.Funasaka, “Implementation issues of parallel downloading methods for a proxy system.” Distributed Computing Systems Workshops, pp. 58– 64, 2005.

Z.Shao, “A qos framework for heavy-tailed traffic over the wireless internet.” MILCOM.Proceedings, pp.1201–1205.

I.Qasem, “Leveraging online social networks for a real-time malware alerting system.” Local Computer Networks (LCN), pp. 272–275, 2013.

C. Yang, R. Harkreader, and G. Gu, “Die free or live hard? empirical evaluation and new design for fighting evolving Twitter spammers,” in Proc. RAID, 2011.

J. Zhang, C. Seifert, J. W. Stokes and W. Lee, “ARROW: Generating signatures to detect drive-by downloads,” in proceedings of the 20th international conference on World Wide Web, ACM, pp.187-196, 2011.

M. A. Rajab, L. Ballard, N. Jagpal, P. Mavrommatis, D. Nojiri, N. Provos, and L. Schmidt, “Trends in circumventing web malware detection,” Google Tech. Rep., 2011.

S. Lee and J. Kim, “Warningbird: Detecting suspicious URLs in Twitter stream,” in Proc. Network and Distributed System Security (NDSS), vol.10, 2012.

C. Whittaker, B. Ryner and M. Nazif, “Large-scale automatic classification of phising pages,” in Proc. NDSS, 2010.

K. Thomas, C. Grier, J. Ma, V. Paxson and D. Song, “Design and evaluation of a real-time URL spam filtering service,” in proceedings Security and Privacy (SP), IEEE Symposium on, pp.447-462, 2011.

S. Chhabra, A. Aggarwal, F. Benevenuto and P. Kumaraguru,

“$oCiaL: the phishing landscape through short URLs,” in proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference CEAS, ACM, pp.92-101, 2011.

“The URL wrapper,” https:// docs/ URLwrapper.

Twitter Help Center, “The Twitter rules,” https:// 18311-the-twitter-rules.

C. Y. R. Harkreader, J. Zhang, S. Shin and G. Gu, “Analyzing

spammers’ social networks for fun and profit-a case study of cyber criminal ecosystem on Twitter,” in Proceedings of the 21st international conference on World Wide Web, ACM, pp.71-80, 2012.

F. Klien and M. Strohmaier, “Short links under attack: geographical analysis of spam in a URL shortener network,” in Proceedings of the 23rd ACM conference on Hypertext and social media, ACM, pp.83-88, 2012.

S. Ghosh, B. Vishwanath, F. Kooti, N. K. Sharma, G. Korlam, F.

Benevenuto, N. Ganguly, and K.P. Gummadi, “Understanding and

combating link farming in the twitter social network,” in Proceedings of the 21st international conference on World Wide Web, ACM, pp.61-70, 2012.

A. Wang, “Don’t follow me: Spam detecting in Twitter,” in Proc.SECRYPT, 2010.

K. Lee, J. Caverlee, and S. Webb, “Uncovering social spammers: Social honeypots + machine learning,” in Proc. 33rd Int’l ACM SIGIR Conf. Research and Development in Information Retrieval, 2010.

C. Grier, K. Thomas, V. Paxson, and M. Zhang, “@spam: The

underground on 140 characters or less,” in proceedings of the 17th ACM conference on Computer and communications security, pp.27-37, 2010.

D. Antoniades, I. Polakis, G. Kontaxis, E. Athanasopoulos, S. Ioannidis, E. P. Markatos, and T. Karagiannis, “Web: The web of short URLs,” in proceedings of the 20th international conference on World Wide Web, pp.715-724, 2011.

“Identifying suspicious URLs: An application of large scale online learning,” in Proc. of the International Conference on Machine Learning, ICML, 2009.

H. Kwak, C. Lee, H. Park, and S. Moon, “What is Twitter, a social network or a news media?” in proceedings of the 19th international conference on World wide web, ACM, pp.591-600, April 2010.

Z. Chu, S. Gianvecchio, H. Wang, and S. Jajodia, “Who is tweeting on

Twitter: Human, bot, or cyborg?” in Proceedings of the 26th annual

computer security applications conference, ACM, pp.21-30, 2010.

G. Stringhini, C. Kruegel, and G. Vigna, “Detecting spammers on social networks,” in proceedings of the 26th Annual Computer Security Applications Conference (ACSAC) pp.1-9, 2010.

F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida, “Detecting spammers on Twitter,” in Proc. Collaboration, electronic messaging, antiabuse and spam conference (CEAS), vol. 6, July 2010.


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