Evolving Connectionist Systems with Evolutionary Self-Optimisatio

Kasabov, Nikola; Chan, Zenobia C.Y.; Song, Qun; Greer, Des

doi:10.1007/3-540-32374-0_9

Cited by 3 publications

(1 citation statement)

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“…These ranges from communication-oriented approaches like authentication protocols over blacklisting to content-based filtering approaches which usually depend on some of Artificial Intelligence (AI) techniques (Bergholz et al, 2010). Current AI algorithms are able to detect phishing e-mail based on fixed features and rules while a few number of machine learning algorithms design to work in online mode (N. Kasabov, 2005). The level of errors in the classification process will increase over time, especially when dealing with unknown zero-day phishing e-mails.…”

Section: Introductionmentioning

confidence: 99%

Phishing Dynamic Evolving Neural Fuzzy Framework for Online Detection “Zero-day” Phishing Email

Almomani¹

2013

IJST

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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.

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Section: Introductionmentioning

confidence: 99%