K-Nearest Neighbor Based URL Identification Model for Phishing Attack Detection
Tsehay Admassu Assegie
Tsehay Admassu Assegie, Department of Computer Science, Faculty of Computing Technology, Aksum Institute of Technology, Aksum University, Axum, Ethiopia.
Manuscript received on 31 March 2021 | Revised Manuscript received on 05 April 2021 | Manuscript Accepted on 15 April 2021 | Manuscript published on 30 April 2021 | PP: 18-21 | Volume-1 Issue-2, April 2021 | Retrieval Number: 100.1/ijainn.B1019021221 | DOI: 10.54105/ijainn.B1019.041221
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Phishing causes many problems in business industry. The electronic commerce and electronic banking such as mobile banking involves a number of online transaction. In such online transactions, we have to discriminate features related to legitimate and phishing websites in order to ensure security of the online transaction. In this study, we have collected data form phish tank public data repository and proposed K-Nearest Neighbors (KNN) based model for phishing attack detection. The proposed model detects phishing attack through URL classification. The performance of the proposed model is tested empirically and result is analyzed. Experimental result on test set reveals that the model is efficient on phishing attack detection. Furthermore, the K value that gives better accuracy is determined to achieve better performance on phishing attack detection. Overall, the average accuracy of the proposed model is 85.08%.
Keywords: Phishing Attack, Machine learning, KNN Network Security, Phishing Detection.
Scope of the Article: Intelligence Applications