School of Computer Science and Informatics

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Browsing School of Computer Science and Informatics by Author "Abdelhamid, Neda"

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    An experimental study of three different rule ranking formulas in associative classification
    (Infonomics Society, 2012) Abdelhamid, Neda; Ayesh, Aladdin, 1972-; Thabtah, Fadi
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    MAC: A Multiclass Associative Classification Algorithm
    (2012-06) Abdelhamid, Neda; Ayesh, Aladdin, 1972-; Thabtah, Fadi; Ahmadi, Samad; Hadi, Wael
    Associative classification (AC) is a data mining approach that uses association rule discovery methods to build classification systems (classifiers). Several research studies reveal that AC normally generates higher accurate classifiers than classic classification data mining approaches such as rule induction, probabilistic and decision trees. This paper proposes a new multiclass AC algorithm called MAC. The proposed algorithm employs a novel method for building the classifier that normally reduces the resulting classifier size in order to enable end-user to more understand and maintain it. Experimentations against 19 different data sets from the UCI data repository and using different common AC and traditional learning approaches have been conducted with reference to classification accuracy and the number of rules derived. The results show that the proposed algorithm is able to derive higher predictive classifiers than rule induction (RIPPER) and decision tree (C4.5) algorithms and very competitive to a known AC algorithm named MCAR. Furthermore, MAC is also able to produce less number of rules than MCAR in normal circumstances (standard support and confidence thresholds) and in sever circumstances (low support and confidence thresholds) and for most of the data sets considered in the experiments.
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    Phishing detection based Associative Classification data mining
    (Elsevier, 2014-03-27) Abdelhamid, Neda; Ayesh, Aladdin, 1972-; Thabtah, Fadi
    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.
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    Prediction phase in associative classification mining.
    (World Scientific Publishing, 2011) Thabtah, Fadi; Hadi, Wael; Abdelhamid, Neda; Issa, Ayman