Details

Autor(en) / Beteiligte

• Abdelhamid, Neda
• Ayesh, Aladdin
• Thabtah, Fadi

Titel

Phishing detection based Associative Classification data mining

Ist Teil von

• Expert systems with applications, 2014-10, Vol.41 (13), p.5948-5959

Ort / Verlag

Amsterdam: Elsevier Ltd

Erscheinungsjahr

2014

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• •The applicability of Associative Classification (AC) on website phishing is investigated.•New structure of the phishing detection approach is developed.•A new AC data mining method that generates multiple labels rules is developed.•Feature assessment of large collection of website features have been carried out.•Experimental results analysis using different algorithms was performed. 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.