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Almomani, Ammar
- Phishing Dynamic Evolving Neural Fuzzy Framework for Online Detection "Zero-day" Phishing Email
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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)Full Text
References
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- 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
Source
Indian Journal of Science and Technology, Vol 7, No 2 (2014), Pagination: 168-173Abstract
This paper proposed ICMPv6 Flood Attack Detection using DENFIS algorithms to detect denial of service (DoS) attacks in IPv6 networks. We developed C# application to send the ICMPv6 flood attack packets the flooding packets were generated using different attack rates starting from 1000 Pings to 1500 Pings, and the normal traffic packets were generated using different ping rates starting from 10 Pings to 15 Pings, for each ICMPv6 Packet, RTT was calculated. The dataset consists of 2000 recorded, which divided into two sets: 80% for training and 20% for testing, the proposed proved that we can detect ICMPv6 Flood Attack with low ischolar_main mean square error which about 0.26.Keywords
Dynamic Evolving Neural Fuzzy Inference System (DENFIS), Denial of service Attack, ICMPv6Full Text