Paper Title : Phishing Page and Malicious URL Detection via Support Vector Machine using Page Layout Feature

ISSN : 2394-2231
Year of Publication : 2022

10.5281/zenodo.6410052

Authors: Dr. E.K. Vellingiriraj ME., Ph.D., G.Savitha

         



MLA Style: Phishing Page and Malicious URL Detection via Support Vector Machine using Page Layout Feature " Dr. E.K. Vellingiriraj ME., Ph.D., G.Savitha " Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: Phishing Page and Malicious URL Detection via Support Vector Machine using Page Layout Feature " Dr. E.K. Vellingiriraj ME., Ph.D., G.Savitha " Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
The web technology has come the corner gravestone of a wide range of platforms, similar as mobile services and smart Internet-of- effects (IoT) systems. In several surroundings, stoner data is aggregated for a pall grounded platform, where web operations are used as a key interface to pierce and configure stoner data. Securing the web interface requires results to deal with pitfalls from both specialized vulnerabilities and social factors. The bushwhackers use web runners visually mimicking licit websites, similar as banking and government services, to collect druggies’ sensitive information. Being phishing defense mechanisms grounded on URLs or runner contents are frequently finessed by bushwhackers. The World Wide Web has come the most essential criterion for information communication and knowledge dispersion. It helps to distribute information timely, fleetly and fluently. Identity theft and identity fraud are appertained as two sides of cyber crime in which hackers and vicious stoner s gain the particular data of being licit druggies to attempt fraud or deception provocation for fiscal gain. E-Mails are used as phishing tools in which licit looking emails are transferred making the genuine druggies identity with licit content with vicious URLs. SpamE-Mails emerges or transforms as Phishing matters. Spoofed Matters plays a vital part in which the hackers pretends to be a licit sender posing to be from a licit association which divulges the stoner to give his particular credentials. The content may escape from Content grounded pollutants or the dispatch may be without any body of the communication except vicious URL in it. This paper identifies vicious URLs in dispatch through reduced point set system. In addition, phishing runners are plant out grounded on CSS attributes values.

Reference
[1] M. Kucherawy and E. Zwicky,Domain-Based Message Authentication,Reporting, Conformance (DMARC), Internet Engineering Task Force,document RFC 7489, Mar. 2015. [Online]. Available: https://www.rfc-editor.org/info/rfc748 [2] A. Bhardwaj, V. Sapra, A. Kumar, N. Kumar, and S. Arthi, ‘‘Why is phish-ing still successful?’’Comput. Fraud Secur., vol. 2020, no. 9, pp. 15– 19,Sep. 2020. [3] O. A. S. Carpinteiro, B. C. Sanches, and E. M. Moreira, ‘‘Detectingimage spam with an artificial neural model,’’Int. J. Comput. Sci. Inf.Secur., vol. 15, no. 1, pp. 296–314, Jan. 2017. [Online]. Available: https://www.academia.edu/36003643/ Journal_of_Computer_Science_ IJCSIS_ January_2017_Full_Volume_pdf [4] Justin Ma, Lawrence Saul, K., Stefan Savage and Geoffrey Voelker, M. “Identifying Suspicious URLs: An Application of Large-Scale Online Learning”, In ICML ’09: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 681-688, 2009. 7 [5] Y. Niu, Y.-M. Wang, H. Chen, M. Ma, and F. Hsu. A Quantitative Study of Forum Spamming Using Context-based Analysis. In Proceedings of the Symposium on Network and Distributed System Security (NDSS), San Diego, CA, Mar. 2007. [6] J. Ma, L. K. Saul, S. Savage, and G. M. Voelker. Identifying Suspicious URLs: An Application of Large-Scale Online Learning. In Proc. of the International Conference on Machine Learning (ICML), Montreal, Quebec, June 2009. [7] Colin Whittaker, Brian Ryner and MarriaNazif, “Large-Scale Automatic Classification of Phishing Pages”, In proceedings of NDSS, 2010. [8] Neil Chou, Robert Ledesma, Yuka Teraguchi, Dan Boneh and John Mitchell, “Client-side defense against web-based identity theft”, In 11th Annual Network and Distributed System Security Symposium (NDSS ’04),San Diego, 2004. [9] G. Casto, O. Fisher, R. Moll, M. Nazif, and D. Born. Client specification for the Google Safe Browsing v2.1 protocol. http://code.google. com/p/google-safe-browsing/wiki/ Protocolv2Spec, 2007. [10] Blake Ross, Collin Jackson, Nicholas Miyake, Dan Boneh, and John Mitchell. A browser plug-in solution to the unique password problem. In Proceedings of 2005 USENIX Security Symposium, 2005. [11] A. Attar, R. M. Rad, and R. E. Atani, “A survey of image spamming andfiltering techniques,”Artificial Intelligence Review, vol. 40, pp. 71– 105,2013. [12] G. Fumera, I. Pillai, and F. Roli, “Spam filtering based on the analysis oftext information embedded into images,”Journal of Machine LearningResearch, vol. 7, pp. 2699–2720, 2006. [13] M. Dredze, R. Gevaryahu, and A. Elias-Bachrach, “Learning fast classifiers for image spam,” inProceedings of the Conference on Email andAnti-Spam (CEAS), 2007. [14] B. Mehta, S. Nangia, M. Gupta, and W. Nejdl, “Detecting image spamusing visual features and near duplicate detection,” inProceedings ofthe International Conference on World Wide Web (WWW), 2008, pp.497–506. [15] T. Liu, W. Tsao, and C. Lee, “A high performance image-spam filteringsystem,” inProceedings of the International Symposium on DistributedComputing and Applications to Business Engineering and Science(DCABES), 2010, pp. 445–449. [16] H. Cheng, Z. Qin, C. Fu, and Y. Wang, “A novel spam image filtering framework with multi-label classification,” inProceedings ofthe International Conference on Communications, Circuits and Systems(ICCCAS), 2010, pp. 282–285. [17] C. Wang, F. Zhang, F. Li, and Q. Liu, “Image spam classificationbased on low-level image features,” inProceedings of the InternationalConference on Communications, Circuits and Systems (ICCCAS), 2010,pp. 290–293. [18] B. Al-Duwairi, I. Khater, and O. Al-Jarrah, “Texture analysisbasedimage spam filtering,” inProceedings of the International Conferencefor Internet Technology and Secured Transactions (ICITST), 2011, pp.288–293.

Keywords
— Data Mining, Phishing Mails, Anti-SPAM Filtering, Phishing Classification.