Open AccessK-Nearest Neighbor Based URL Identification Model for Phishing Attack Detection
Author(s) -
Tsehay Admassu Assegie
Publication year - 2021
Publication title -
indian journal of artificial intelligence and neural networking (ijainn)
Language(s) - English
Resource type - Journals
ISSN - 2582-7626
DOI - 10.54105/ijainn.b1019.041221
Subject(s) - phishing , computer science , database transaction , transaction data , identification (biology) , computer security , data mining , the internet , world wide web , database , botany , biology
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%.