Refine your search
Collections
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Altaher, Altyeb
- Phishing Dynamic Evolving Neural Fuzzy Framework for Online Detection "Zero-day" Phishing Email
Abstract Views :470 |
PDF Views:128
Authors
Affiliations
1 National Advanced IPv6 Centre (NAV6), Universiti Sains Malaysia, 11800 USM, Penang, MY
2 Department of Computer Science, University of New Brunswick, CA
1 National Advanced IPv6 Centre (NAV6), Universiti Sains Malaysia, 11800 USM, Penang, MY
2 Department of Computer Science, University of New Brunswick, CA
Source
Indian Journal of Science and Technology, Vol 6, No 1 (2013), Pagination: 3960-3964Abstract
Phishing is a kind of attack in which criminals use spoofed emails and fraudulent web sites to trick financial organization and customers. Criminals try to lure online users by convincing them to reveal the username, passwords, credit card number and updating account information or fill billing information. One of the main problems of phishing email detection is the unknown “zero-day” phishing attack, (we define zero-day attacks as attacks that phisher mount using hosts that do not appear in blacklists and not trained on the old data sample and it is a noise data), which increases the level of difficulty to detect phishing email. Nowadays, phishers are creating different representation techniques to create unknown “zero-day” phishing email to breach the defenses of those detectors. Our proposed is a novel framework called phishing dynamic evolving neural fuzzy framework (PDENF), which adapts the evolving connectionist system (ECoS) based on a hybrid (supervised/unsupervised) learning approach. PDENF adaptive online is enhanced by offline learning to detect dynamically the phishing email included unknown zero-day phishing e-mails before it get to user account. PDENF is suggested to work for high-speed “life-long” learning with low memory footprint and minimizes the complexity of the rule base and configuration with few number of rules creation for email classification. We expect to achieves high performance, including high level of true positive, true negative, sensitivity, precision, F-measure and overall accuracy compared with other approaches.Keywords
Phishing Email, Detection, Zero-day, Evolving Connectionist System (Ecos)References
- Abu-Nimeh, S., D. Nappa, et al. (2007). A comparison of machine learning techniques for phishing detection. Proceedings of the eCrime Researchers Summit,, Pittsburgh, PA,, ACM.
- Almomani, A., T.-C. Wan, et al. (2011). “An Online Model on Evolving Phishing E-mail Detection and Classification Method.” journal of applied science 11(18): 3301-3307.
- Almomani, A., T.-C. Wan, et al. (2012). “An enhanced online phishing e-mail detection framework based on “evolving connectionist system”.” International Journal of Innovative Computing, Information and Control (IJICIC) 9(2).
- Almomani, A., T.-C. Wan, et al. (2012). “Evolving Fuzzy Neural Network for Phishing Emails Detection.” Journal of Computer Science 8(7): 1099-1107.
- Almomani, A., T.-C. Wan, et al. “Asurvay of learning-based techniques of phishing email filtering “ International Journal of Digital Content Technology and its Applications(JDCTA), vol. 6,no.18, 2012
- APWG (2010). “Phishing Activity Trends Report “. from http://www.antiphishing.org/reports/apwg_report_Q1_2010.pdf.
- Bergholz, A., J. De Beer, et al. (2010). “New filtering approaches for phishing email.” Journal of computer security 18(1): 7-35.
- Bimal Parmar, F. (2012). “Protecting against spear-phishing.” Computer Fraud & Security 2012(1): 8-11.
- Cook, D. L., V. K. Gurbani, et al. (2009). “Phishwish: a simple and stateless phishing filter.” Security and Communication Networks 2(1): 29-43.
- Dunlop, M., S. Groat, et al. (2010). GoldPhish: Using Images for Content-Based Phishing Analysis, IEEE.
- Fette, I., N. Sadeh, et al. (2007). Learning to detect phishing emails. Proceedings of the 16th International World Wide Web Conference (WWW 2007), Banff, Alberta, Canada, ACM Press.
- GARTNER (2007). “Gartner Survey Shows Phishing Attacks Escalated in 2007; More than $3 Billion Lost to These Attacks.” Retrieved December 17, from http://www.gartner.com/it/page.jsp?id=565125.
- Y. C. and Q. Song (2009). “Dynamic neural fuzzy inference system.” Advances in Neuro-Information Processing 5506/2009: 1245-1250.
- IID (2011). eCrime Trends Report. 1.
- Islam, M. R., J. Abawajy, et al. (2009). Multi-tier phishing email classification with an impact of classifier rescheduling. the International Symposium on Pervasive Systems, Algorithms, and Networks, Kaohsiung, Taiwan IEEE.
- Kasabov, L. B. a. N. (2007). EVOLVING CONNECTIONIST SYSTEM(ECOS). Computational Neurogenetic Modeling SPRINGER.
- Kasabov, N. and Q. Song (2002). “DENFIS: dynamic evolving neural- fuzzy inference system and its application for time-series prediction.” Fuzzy Systems, IEEE Transactions on 10(2): 144-154.
- Khonji, M., Y. Iraqi, et al. (2012). “Enhancing Phishing E-Mail Classifiers: A Lexical URL Analysis Approach.” International Journal for Information Security Research (IJISR) 2(1/2).
- Khonji, M., A. Jones, et al. (2011). A novel Phishing classification based on URL features. GCC Conference and Exhibition (GCC), 2011 IEEE.
- MAAWG (2011). Messaging Anti-Abuse Working Group (MAAWG) Email Metrics Program. 15. third Quarter.
- N. Kasabov, Z. S. H. C., Q. Song and D. Greer (2005). Evolving Connectionist Systems with Evolutionary Self-Optimisation springer. 173.
- Saberi, A., M. Vahidi, et al. (2007). Learn to Detect Phishing Scams Using Learning and Ensemble? Methods. IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Workshops (IAT 07), pp. 311-314, Silicon Valley, USA, IEEE.
- snjezana soltic, l. p. (2006). “Bulletin of Applied Computing and Information Technology “ Journal of Applied Computing and Information Technology 4(1).
- Song, Q. and N. Kasabov (2001). ECM-A novel on-line, evolving clustering method and its applications. Proceedings of the Fifth Biannual Conference on Artificial Neural Networks and Expert Systems (ANNES2001),, New Zealand, Dunedin.
- Toolan, F. and J. Carthy (2010). Feature selection for Spam and Phishing detection. eCrime Researchers Summit (eCrime), 2010, IEEE.
- ICMPv6 Flood Attack Detection using DENFIS Algorithms
Abstract Views :244 |
PDF Views:0
Authors
Affiliations
1 National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, 11800 USM, Penang, MY
2 National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, 11800 USM, Penang
3 Department of Computer Engineering, National Institute of Technology Kurukshetra, MY
1 National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, 11800 USM, Penang, MY
2 National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, 11800 USM, Penang
3 Department of Computer Engineering, National Institute of Technology Kurukshetra, MY