Phishing detection based Associative Classification data mining

Neda Abdelhamid*, Aladdin Ayesh, Fadi Thabtah

*Corresponding author for this work

    Research output: Contribution to journalReview articlepeer-review

    228 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)5948-5959
    Number of pages12
    JournalExpert Systems with Applications
    Issue number13
    Early online date27 Mar 2014
    Publication statusPublished - 1 Oct 2014


    • Classification
    • Data mining
    • Forged websites
    • Internet security
    • Phishing


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