The International Arab Journal of Information Technology (IAJIT)

..............................
..............................
..............................


Intelligent Association Classification Technique for Phishing Website Detection

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.


[1] Abdelhamid N., Ayesh A., and Hadi W., “Multi- Label Rules Algorithm Based Associative Classification,” Parallel Processing Letters, vol. 24, no. 1, pp. 1-21, 2014.

[2] Abdelhamid N., Ayesh A., and Thabtah F., “Emerging Trends in Associative Classification Data Mining,” International Journal of Electronics and Electrical Engineering, vol. 3, no. 1, pp. 50-53, 2015.

[3] Ajlouni M., Hadi W., and Alwedyan J., “Detecting Phishing Websites Using Associative Classification,” European Journal of Business and Management, vol. 5, no. 15, pp. 36-40, 2013.

[4] Al-Fayoumi M., “Enhanced Associative Classification Based on Incremental Mining Algorithm,” International Journal of Computer Science Issues, vol. 12, no. 1, pp. 124-130, 2015.

[5] Alazaidah R., Thabtah F., and Al-Radaideh Q., “A Multi-Label Classification Approach Based on Correlations Among Labels,” International Journal of Advanced Computer Science and Applications, vol. 6, no. 2, pp. 52-59, 2015.

[6] Alwidian J., Hadi W., Salam M., and Mansour H., “Categorize Arabic Data Sets Using Multi- Class Classification Based on Association Rule Approach,” in Proceedings of the International Conference on Intelligent Semantic Web- Services and Applications, Amman, pp. 18, 2011.

[7] Alwidian J., Hammo B., and Obeid N., “WCBA: Intelligent Association Classification Technique for Phishing Website Detection 495 Weighted Classification Based on Association Rules Algorithm for Breast Cancer Disease,” Applied Soft Computing, vol. 62, pp. 536-549, 2018.

[8] Alwidian J., Hammo B., and Obeid N., “Enhanced CBA algorithm Based on Apriori Optimization and Statistical Ranking Measure,” in Proceeding of the 28th International Business Information Management Association, Seville, pp. 4291-4306, 2016.

[9] Alwidian J., Hammo B., and Obeid N., “FCBA: Fast Classification Based on Association Rules Algorithm,” International Journal of Computer Science and Network Security, vol. 16, no. 12, pp. 117-127, 2016.

[10] Brooks J., “Anti-Phishing Best Practices: Keys to Aggressively and Effectively Protecting Your Organization from Phishing Attacks,” White Paper, Cyveillance, 2006.

[11] Gupta S. and Singhal A., “Dynamic Classification Mining Techniques for Predicting Phishing URL,” in Proceeding of Soft Computing: Theories and Applications, Singapore, pp. 537-546, 2018.

[12] Hadi W., “EMCAR: Expert Multi Class Based on Association Rule,” International Journal of Modern Education and Computer Science, vol. 5, no. 3, pp. 33-41, 2013.

[13] Hadi W., Aburub F., and Alhawari S., “A New Fast Associative Classification Algorithm for Detecting Phishing Websites,” Applied Soft Computing, vol. 48, pp. 729-734, 2016.

[14] Hadi W., Salam M., and Al-Widian J., “Performance of NB and SVM Classifiers in Islamic Arabic Data,” in Proceedings of the 1st International Conference on Intelligent Semantic Web-Services and Applications, Amman, pp. 1-6, 2010.

[15] Hota H., Shrivas A., and Hota R., “An Ensemble Model for Detecting Phishing Attack with Proposed Remove-Replace Feature Selection Technique,” Procedia Computer Science, vol. 132, pp. 900-907, 2018.

[16] Kulkarni M., Varma K., Patel S., Mer U., Parmar S., and Mahajan A., “A Study of Phishing Detection Using Associative Data Mining,” International Journal Of Scientific Research in Science, Engineering and Technology, vol. 4, no. 5, pp. 419-423, 2018.

[17] Li W., Han J., and Pei J., “CMAR: Accurate and Efficient Classification Based on Multiple Class- Association Rules,” in Proceedings of IEEE International Conference on Data Mining, San Jose, pp. 369-376 2001.

[18] Liu B., Hsu W., and Ma Y., “Integrating Classification and Association Rule Mining,” in Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, New York, pp. 80-86,1998.

[19] Ma B., Zhang H., Chen G., Zhao Y., and Baesens B., “Investigating Associative Classification for Software Fault Prediction: An Experimental Perspective,” International Journal of Software Engineering and Knowledge Engineering, vol. 24, no. 1, pp. 61-90, 2014.

[20] Magazine F., Online Shopping Worldwide Ecommerce Statistics, Last visited, 2011.

[21] Parekh S., Parikh D., Kotak S., and Sankhe S., “A New Method for Detection of Phishing Websites: URL Detection,” in Proceeding of International Conference on Inventive Communication and Computational Technologies, Coimbatore, pp. 949-952, 2018.

[22] Pereira R., Plastino A., Zadrozny B., and Merschmann L., “Categorizing Feature Selection Methods for Multi-Label Classification,” Artificial Intelligence Review, vol. 49, no. 1, pp. 57-78, 2018.

[23] Rao C., Ramana A., and Sowmya B., “Detection of Phishing Websites Using Hybrid Model,” GPH-Journal of Computer Science and Engineering, vol. 1, no. 1, pp. 15-22, 2018.

[24] Sankhyan R., Shetty A., Dhanopia L., Kaspale C., and Dantal P., “PDS-Phishing Detection Systems,” International Research Journal of Engineering and Technology, vol. 5, no. 4, pp. 2429-2431, 2018.

[25] Sriramoju S., Ramesh G., and Srinivas B., “An Overview of Classification Rule and Association Rule Mining,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 3, no. 1, pp. 1962- 1970, 2018.

[26] Tan P., Steinbach M., Kumar V., Taware S., Ghorpade C., Shah P., Lonkar N., and Bk M., “Phish Detect: Detection of Phishing Websites Based on Associative Classification (AC),” International Journal of Advanced Research in Computer Science Engineering and Information Technology, vol. 4, no. 3, pp. 384-395, 2015.

[27] Taware S., Ghorpade C., Shah P., Lonkar N., and Bk M., “Phish Detect: Detection of Phishing Websites Based on Associative Classification (AC),” International Journal of Advanced Research in Computer Science Engineering and Information Technology, vol. 4, no. 3, pp. 384- 395, 2015.

[28] Wadhawan R., “Prediction of coronary heart disease using Apriori algorithm with data mining classification,” International Journal of Research in Science and Technology, vol. 3, no. 1, pp. 1-15, 2018.

[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.