Design the Framework for Detecting Malicious Mobile Web- pages in Real Time

Authors(2) :-Sushma K, Dr. K. Thippeswamy

Mobile specific web pages differ significantly from their desktop counterparts in content, layout and functionality. Accordingly, existing techniques to detect malicious websites are unlikely to work for such web pages. The disclosed technology includes techniques for identifying malicious mobile electronic documents, e.g. web pages or emails, based on static document features. In this paper, we design and implement kAYO, a mechanism that distinguishes between malicious and benign mobile web pages. kAYO makes this determination based on static features of a webpage ranging from the number of iframes to the presence of known fraudulent phone numbers. We then apply kAYO to a dataset of over 350,000 known benign and malicious mobile web pages and demonstrate 90% accuracy in classification. Moreover, we discover, characterize and report a number of web pages missed by Google Safe Browsing and Virus Total, but detected by kAYO.

Authors and Affiliations

Sushma K
M.Tech in CS&E, VTU PG Centre, Mysuru, Karnataka, India
Dr. K. Thippeswamy
Professor and Chairman, DoS in CS&E, VTU PG Centre, Mysuru, Karnataka, India

[1]. Chaitrali Amrutkar, Young Seuk Kim, and Patrick Traynor, Senior Member, IEEE “Detecting Mobile Malicious

Webpages in Real Time” Chaitrali Amrutkar, Young Seuk Kim, and Patrick Traynor, Senior Member, IEEE

[2]. Charles Arthur, “Mobile internet devices ’will outnumber humans this year’.” http://www.theguardian. com/ technology/ 2013/feb/07/ mobile-internet-outnumber-people.

[3]. Chakradeo, S., Reaves, B., Traynor, P., and Enck, W., “MAST: Triage for Market-scale Mobile Malware Analysis,” Tech. Rep. GT-CS-12-01, College of Computing, Georgia Institute of Technology, 2012.

[4]. N. Provos, P. Mavrommatis, M. A. Rajab and F. Monrose, “All Your iFRAMEs Point to Us”, Proceedings of the 17th Conference on Security Symposium, SS, USENIX Association Berkeley, (2008); CA,USA.

[5]. D. Canali, M. Cova, G. Vigna, and C. Kruegel. Prophiler: a fast filter for the large-scale detection of malicious webpages. In Proceedings of the 20th International Conference on World Wide Web (WWW), 2011.

[6]. L. Bilge, E. Kirda, C. Kruegel, and M. Balduzzi. EXPOSURE: Finding malicious domains using passive DNS analysis. In Proceedings of the 18th Annual Network and Distributed System Security Symposium (NDSS), 2011.

[7]. A. P. Felt and D. Wagner. Phishing on mobile devices. In Web 2.0 Security and Privacy (W2SP), 2011.

[8]. “Cross-site Scripting (XSS) Attacks and Defense

Mechanisms: classification and state-of-art” by Shashank Gupta and B.B Gupta ,14 September,2015, Springer.

[9]. Dr. Jitendra Agrawal, Dr. Shikha Agrawal, Anurag Awathe, Dr. Sanjeev Sharma. “Malicious Web Page Detection through Classification Technique: A Survey”. In Proceeding of the IJCST March 2017.

Publication Details

Published in : Volume 4 | Issue 6 | May-June 2018
Date of Publication : 2018-05-08
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 01-03
Manuscript Number : CSEIT184601
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Sushma K, Dr. K. Thippeswamy, " Design the Framework for Detecting Malicious Mobile Web- pages in Real Time", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 6, pp.01-03, May-June.2018

Follow Us

Contact Us