Phishing detection based associative classification data mining

Abdelhamid, Neda
Ayesh, Aladdin
Thabtah, Fadi
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Elsevier Ltd
Website phishing is considered one of the crucial security challenges for the online community due to the massive numbers of online transactions performed on a daily basis. Website phishing can be described as mimicking a trusted website to obtain sensitive information from online users such as usernames and passwords. Black lists, white lists and the utilisation of search methods are examples of solutions to minimise the risk of this problem. One intelligent approach based on data mining called Associative Classification (AC) seems a potential solution that may effectively detect phishing websites with high accuracy. According to experimental studies, AC often extracts classifiers containing simple "If-Then" rules with a high degree of predictive accuracy. In this paper, we investigate the problem of website phishing using a developed AC method called Multi-label Classifier based Associative Classification (MCAC) to seek its applicability to the phishing problem. We also want to identify features that distinguish phishing websites from legitimate ones. In addition, we survey intelligent approaches used to handle the phishing problem. Experimental results using real data collected from different sources show that AC particularly MCAC detects phishing websites with higher accuracy than other intelligent algorithms. Further, MCAC generates new hidden knowledge (rules) that other algorithms are unable to find and this has improved its classifiers predictive performance. © 2014 Elsevier Ltd. All rights reserved.
This review is not available at CUD collection. The version of scholarly record of this review is published in Information Sciences (2014), available online at:
Classification (of information), Data mining, Websites, Associative classification, Intelligent Algorithms, Internet security, Phishing, Phishing detections, Predictive accuracy, Predictive performance, Sensitive information, Computer crime
Abdelhamid, N., Ayesh, A., & Thabtah, F. (2014). Phishing detection based Associative Classification data mining. Expert Systems with Applications, 41(13), 5948–5959.