The usages of internet and increasing volume of internet users force us to think about the current cyber security and its infrastructure. There are a number of different kinds of attack deployed using the internet among the phishing is one of the most serious attack conditions. In this condition an innocent user can lose their financial status or social credibility. The phishing attacker still the users private, sensitive and confidential information, additionally usages these data to harm the target person. Therefore it is a serious criminal offence. In this context a number of techniques are developed for resolving the issues of phishing attacks, but most of them are not much effective due to changing strategies of the phishing attackers. The phishing attacks are mostly deployed using the malicious and forged URLs. Thus the pattern recognition of these URLs can help us to resolve the phishing attacks. In this presented work a data mining based phishing URL classification technique is proposed for design and implementation. The proposed technique usages the phish tank database for obtaining the knowledge about the phishing URL properties and then using these properties the data mining system prepare the rules for identifying the target phishing URLs. In this context the ARM algorithm is employed. The Arm algorithm first prepares the association rules using the apriori algorithm. After generation of association rules the confidence based score are used to label each rule to a score values. Finally on the basis of score threshold the unfruitful rules are pruned. The remaining rules are used for classification task. The proposed technique is implemented and their performance is measured, according to the gained performance the proposed technique is accurate and efficient as compared to the traditional apriori algorithm based classification technique.

phishing attack, malicious URL classification, association rule mining, rule based classification, ARM algorithm, outlier removal

[1] S. Carolin Jeeva and Elijah Blessing Rajsingh, “Intelligent phishing url detection using association rule mining”, Hum. Cent. Comput. Inf. Sci. (2016) 6:10, DOI 10.1186/s13673-016-0064-3
[2] Gorunescu, F, Data Mining: Concepts, Models, and Techniques, Springer, 2011.
[3] Han, J., and Kamber, M., Data mining: Concepts and techniques, Morgan-Kaufman Series of Data Management Systems San Diego: Academic Press, 2001.
[4] Neelam adhab Padhy, Dr. Pragnyaban Mishra and Rasmita Panigrahi, “The Survey of Data Mining Applications and Feature Scope, International Journal of Computer Science, Engineering and Information Technology (IJCSEIT)”, vol.2, no.3, June
[5] Manoj and Jatinder Singh, “Applications of Data Mining for Intrusion Detection”, International Journal of Educational Planning & Administration. Volume 1, Number 1 (2011), pp. 37-42
[6] M. Rajalakshmi, M. Sakthi, “Max-Miner Algorithm Using Knowledge Discovery Process in Data Mining”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 11, November 2015
[7] Smriti Srivastava & Anchal Garg, “Data Mining For Credit Card Risk Analysis: A Review”, International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR), Vol. 3, Issue 2, Jun 2013, 193-200
[8] Dipti Verma and Rakesh Nashine, “Data Mining: Next Generation Challenges and Future Directions”, International Journal of Modeling and Optimization, Vol. 2, No. 5, October 2012
[9] Neelamadhab Padhy, Dr. Pragnyaban Mishra, “The Survey of Data Mining Applications and Feature Scope”, International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), PP. 43-58 Vol.2, No.3, June 2012.
[10] Watson, D., Holz, T., and Mueller, S. (2005) Know your enemy: Phishing, behind the scenes of Phishing attacks, The Honeynet Project & Research Alliance
[11] Jagatic, T., Johnson, N., Jakobsson, M., and Menczer, F. (2007) Social Phishing, Community. ACM, Vol. 50, No. 10 (pp. 94-100).