Abstract
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.
Highlights
Email has been an online ‘killer application’ utilized by people, businesses, Governments and different organizations for the needs of communicating, sharing and distributing data (MAAWG, 2011)
Phishing email is a subset of spam which is related to social engineering schemes, which depends on forged e-mails and through an embedded link within the e-mail, the phisher tries to redirect users to fake Websites
The current approaches have many problems to deal with phishing email included unknown “zero-day” attack, which causes high level of false positives (FPs), false negatives (FNs) and low level of accuracy in classification process
Summary
Email has been an online ‘killer application’ utilized by people, businesses, Governments and different organizations for the needs of communicating, sharing and distributing data (MAAWG, 2011). Phishing email is a subset of spam which is related to social engineering schemes, which depends on forged e-mails (i.e. claims that originated from a legitimate company or bank) and through an embedded link within the e-mail, the phisher tries to redirect users to fake Websites. Phishing e-mail detection has been a major area of focus in a number of studies In this proposed a framework called Phishing Dynamic Evolving Neural Fuzzy Framework (PDENFF) is proposed, where a dynamic process is one that continuously changes www.indjst.org 122. The proposed framework can detect phishing email by evolving stream data mining that leads to improve classification performance. It has a high level of performance and characterized by life-long learning with low memory footprint
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