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    Phishing detection based Associative Classification data mining

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    Date
    2014-03-27
    Author
    Abdelhamid, Neda;
    Ayesh, Aladdin, 1972-;
    Thabtah, Fadi
    Metadata
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    Abstract
    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.
    Description
    Citation : Abdelhamid, N., Ayesh, A. and Thabtah, F. (2014) Phishing detection based Associative Classification data mining. Expert Systems with Applications, 41 (13), pp. 5948–5959
    URI
    http://hdl.handle.net/2086/14814
    DOI
    http://dx.doi.org/10.1016/j.eswa.2014.03.019
    Research Group : Mobile Cognitive Systems Research Group
    Research Institute : Cyber Technology Institute (CTI)
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
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    • School of Computer Science and Informatics [2966]

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