Intelligent phishing detection parameter framework for E-banking transactions based on Neuro-fuzzy

Proceedings of 2014 Science and Information Conference, SAI 2014

Volumn , Issue , 2014, Pages 545-555

  Barraclough, P A ^a   Hossain, M A ^a   Sexton, G ^a   Aslam, N ^a  

^a NORTHUMBRIA UNIVERSITY   (United Kingdom)

Author keywords

FIS; fuzzy inference system; Intelligent phishing detection; neuro fuzzy

Indexed keywords

COMPUTER CRIME; ELECTRONIC COMMERCE; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; LEARNING ALGORITHMS; MACHINE LEARNING; MEMBERSHIP FUNCTIONS; NEURONS;

CROSS-VALIDATION METHODS; FUZZY INFERENCE SYSTEMS; NEURO-FUZZY; NEURON FUZZY SYSTEMS; PARAMETER FRAMEWORK; PHISHING DETECTIONS; REAL TIME SOLUTION; SYSTEMS PERFORMANCE;

FUZZY INFERENCE;

EID: 84909631007     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SAI.2014.6918240     Document Type: Conference Paper

References (30)

1. Financial Fraud Action UK, Cheque &Credit clearing Company, UKCARDS Association. Deception crimes drive small increase in card fraud and online banking fraud losses. Press Release, pp. 2, 2012[online] www. financialfraudaction. org. uk. Accessed 24. 7. 2013.
2. M. Aburrous, M. A. Hossain, K. Dahal, and F. Thabtah, "Modelling Intelligent Phishing Detection System for e-Banking using Fuzzy data Mining, " International, 2009.
3. N. Sanglerdsinlapachai, and A. Rungsawang, "Using Domain Top-page Similarity Feature in machine learning-based Web Phishing Detection, " In Procedings of IEEE 3rd International Conference on knowledge Discovery and Data Mining, pp. 187-190, 2010.
4. G. Xiang, B. A. Pendleton, J. Hong, "Modelling content from humanverified blacklist for accurate zero-hour phish detection, " probabilistic approach for zero hour phish detection, In Proceedings of the 15th European, 2009.
5. J. Ma, L. Saul, S. Savag, G. Voelker, "Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs, " Proc. of the 15th International Conference on Knowledge Discovery and Data Mining, Paris, France, pp. 1245-1254, 2009.
6. PhishTank Site Checker (2013), GS! Networks, [online] https: //addons. mozilla. org/en-US/firefox/addon/phishtanksitechecker/ reviews/ Accessed 22. 2. 2014.
7. C. Whittaker, B. Ryner, M. Nazif, "Large-Scale Automatic Classification of Phishing Pages, " In the 17th Annual Network and Distributed System Security {NDSS'10} Symposium, 2010.
8. S. Sheng, B. Wardman, G. Warner, L. Cranor, J. Hong, C. Zhang, "An empirical analysis of phishing blacklists" in Proceedings of the 6thConference on Email and Anti-Spam, 2009.
9. J. S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference system. IEEE, ". Transactions on systems, MAN, and Cybernetics, Vol. 23, No. 3, 1993.
10. European Union Agency for Network and Information Security (ENISA), (2005-2014), [Online] https: //www. enisa. europa. eu/aboutenisa Accessed 20. 1. 2014.
11. R. Suriya, K. Saravanan, A. Thangavelu, "An integrated approach to detect phishing mail attacks a case study, " SIN Proceedings of the 2nd international conference on Security of information and networks, north Cyprus, Turkey, October 2009, 6-10, pp. 193-199, vol. 3 ACM New York.
12. L. Wenyin, N. Fang, X. Quan, B. Qiu, G. Liu, "Discovering Phishing Target based on Semantic Link Network, " Future Generation Computer Systems, Elsevier, Volume 26, Issue 3, March 2010, pp. 381-388.
13. G. Xiang, B. A. Pendleton, J. I. Hong, C. P. Rose, "A hierarchical adaptive, " Symposium on Research in Computer Security (ESORICS'10). 268-285, 2010.
14. X. Dong, J. A. Clerk, J. L. Jacob, "Defending the weakest link: Phishing Website Detection by analysing User Behaviours, " IEEE Telecommun System, 45: pp. 215-226, 2010.
15. B. Wardman, T. Stallings, G. Warner, A. Skjellum, "High-Performance Content-Based Phishing Attack Detection, " eCrime Researchers Summit (eCrime), pp. 1-9, Conference: 7-9 Nov. 201 1, San Diego, CA.
16. A. P. Barraclough, M. A. Hossain, M. A. Tahir, G. Sexton, N. Aslam "Intelligent phishing detection and protection scheme for online transactions, " Expert Systems with Application 40, pp. 4697-4706, 2013.
17. A. Le, A. Markopoulou, M. Faloutsos, "Phishdef: Url names say it all, " INFOCOM, Proceedings IEEE, pp. 191-195, 2010.
18. H. Huh, H. Kim, "Phishing Detection with popular search engine: Simple and effective", In Proceeding FPS'11 Proceedings of the 4th Canada-France MITACS conference on Foundations and Practice of Security, pp 194-207, 2012.
19. D. Canali, M, Cova, G. Vigna, C. Krugel "Prophiler: A fast filter for the large-scale detection of malicious web pages, " In Proceedings of the International World Wide Web Conference., 2011.
20. G. Xiang, J. Hong, C. P. Rose, L. Cranor, " Cantina+: A feature-rich machine learning framework for detecting phishing web sites, " ACM Transactions on Information and System Security (TISSEC), 14(2), pp. 2-21, 2011.
21. W. Ead, W. Abdelwahed, H. Abdul-Kader, "Adaptive Fuzzy Classification-Rule Algorithm in Detection Malicious Web Sites from Suspicious URLS, " International Arab Journal of e-Technology 3 (1), pp. 1-9, 2013.
22. G. Xiang, "Toward a phish free world: A feature-type-aware cascaded learning framework for phish detection", Thesis, Language technologies institute, School of computer science., 2013.
23. J. T. Ma, "Learning to detect malicious URLs, " Thesis, University of California, 2010.
24. P. B. Sivarao, N. S. M. El-Tayeb, "A New Approach of Adaptive Network-Based Fuzzy Inference System Modelling in Laser Processing A Graphical User Interface (GUI) Based, " Journal of Computer Science. 5 (10), pp. 704-710, 2009.
25. Complying with anti-phishing regulation (2012) http: //help. wildapricot. com/display/DOC/Complying+with+antispam+ regulations
26. PhishTank, "Join the fight against phishing, " 2012. [online] http: //www. phishtank. com/ Accessed 5. 7. 2013 and 10. 7. 2013.
27. S. Abu-Nimeh, D. Nappa, X. Wang, and S. Nair, "A Comparison of Machine Learning Techniques for Phishing Detection". In Proceedings of the Anti-Phishing Working Group eCrime Researchers Summit, Pittsburgh, PA, Oct. 2007.
28. H. A. Martens and P. Dardenne "Validation and verification of regression in small data sets. 44: pp. 99-121, 1998.
29. M. Young, The Technical Writer's Handbook. Mill Valley, CA: University Science, 1989.
30. M. Aburrous, M. A. Hossain, K. Dahal and F. Thabtah "Intelligent phishing detection system for e-banking using fuzzy data mining, "Expert Systems with Applications 37, pp. 7913-7921, 2010