The week's pick
Reseach Article
A Data Mining approach to Deal with Phishing URL Classification Problem
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 41 |
| Year of Publication: 2019 |
| Authors: Sonam Saxena, Amit Shrivastava, Vijay Birchha |
10.5120/ijca2019919319
|
Sonam Saxena, Amit Shrivastava, Vijay Birchha . A Data Mining approach to Deal with Phishing URL Classification Problem. International Journal of Computer Applications. 178, 41 ( Aug 2019), 44-49. DOI=10.5120/ijca2019919319
Abstract
Data mining is readily growing and accepted technology in recent years. It is utilized for finding instant decisions by analyzing the historical records. A formal decision making technique can also be helpful for information security. In this presented work the demonstration of a data mining application is provided. The proposed data mining application contributes on the information security. Therefore URL classification problem is taken in consideration. In this context we can apply here the any supervised learning algorithm but in this work the association rule mining based technique is proposed for solving the URL classification. That technique is used for analyzing the URL patterns of two kinds of class labels i.e. phishing and legitimate. In this context a rule based classification technique is proposed. That technique is computing the association rules and we can use these patterns to classify the URL data. The Idea is taken from [1] where apriori algorithm is implemented for generation and classification of phishing URLs. Apriori algorithm is computationally complex and requires significant amount of time and memory for generating candidate sets. Therefore we usages the FP-Tree algorithm which efficient develops the association rules with less resource requirements. The system can be used for designing the phishing tool bars. This technique is used with the phish tank dataset with different set of data for experimentations. The obtained results shows the proposed technique requires less amount of time and memory. In near future it is tried to reduce time and improve the accuracy of the proposed phishing URL classification system.
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