Abstract Many critical applications need more accuracy and speed in the decision making process. Data mining scholars developed set of artificial automated tools to enhance the entire decisions based on type of application. Phishing is one of the most critical application needs for high accuracy and speed in decision making when a malicious webpage impersonates as legitimate webpage to acquire secret information from the user. In this paper, we proposed a new Association Classification (AC) algorithm as an artificial automated tool to increase the accuracy level of the classification process that aims to discover any malicious webpage. An Intelligent Association Classification (IAC) algorithm developed in this article by employing the Harmonic Mean measure instead of the support and confidence measure to solve the estimation problem in these measures and discovering hidden pattern not generated by the existing AC algorithms. Our algorithm compared with four well-known AC algorithm in terms of accuracy, F1, Precision, Recall and execution time. The experiments and the visualization process show that the IAC algorithm outperformed the others in all cases and emphasize on the importance of the general and specific rules in the classification process.

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[29] Witten L., Frank E., Hall M., and Pal C., Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2005. 496 The International Arab Journal of Information Technology, Vol. 17, No. 4, July 2020 Mustafa Al-Fayoumi received a BSc degree in Computer Science from Yarmouk University, Irbid, Jordan, in 1988. He earned an MSc degree in Computer Science from the University of Jordan, Amman, Jordan, in 2003, and his PhD in Computer Science from the Faculty of Science and Technology at Anglia University, UK, in 2009. Currently, he is the Dean’s Assistant for King Hussein School of computing sciences at Princess Sumaya University for Technology (PSUT), Jordan, His research interests include computer security, cryptography, identification and authentication, wireless and mobile networks security, e-application security, simulation and modelling, algorithm analyses and design, information retrieval, data mining and other related topics. Jaber Alwidian holds a PhD in Computer Science (The University of Jordan). He received his B.Sc. degree in Computer Information System from the University of Philadelphia and M.Sc. degree in Information System from the Jordan University in 2005 and 2010, respectively. He has about seven years of work experience as a lecturer and one year as a big data scientist (INTRASOFT Middle East/big data department). His research interests are data mining, software engineering and image processing. Mohammad Abusaif received a BSc degree in Computer Science from the University of Jordan, Amman, Jordan, in 1994. He is a senior level Manager with 24 years’ experience working within the IT industry and software development, ERP, EMR and Big Data implementation of complex business systems in Jordan, United Arab Emirates, Kingdom of Saudi Arabia, Qatar, Oman, Lebanon and Yemen using various software and hardware platforms. Experienced at building, managing, motivating and leading multi-cultural teams, both local and globally distributed, while delivering complex projects covering various business sectors including: Government sector, Constructions, private sector, Healthcare, Retail and Distribution Business.