Comparative Study of KNN and LR Approaches of Machine Learning with Respect to the Identification of Phishing Websites
Keywords:
KNN modal ML, LR model ML, , Phishing and Non Phishing Websites identification using KNN, Phishing and Non Phishing Websites identification using LRAbstract
With the advent of Internet and growth in the field of Information and Communication technology, phishing attacks are becoming very common source for finding users personal or confidential information. These types of attacks are executed through email, websites, instant messaging services etc. This type of attack is very common and is also considered as one of the major threats to the organization. Therefore, it becomes very important for an individual to check if the message has been received from the trusted sender, as it fools the victim by pretending to be the original user and asking them to share their personal and confidential information. There are lots of techniques which are used to detect phishing websites. In this paper, the two machine learning classification algorithms: K-Nearest Neighbors (KNN) and Logistic Regression (LR) are applied to the phishing and non-phishing website URLs dataset. The performance of classification algorithms KNN and LR are compared by using the classification report accuracy, precision, confusion matrix, sensitivity, f-score and time required for its execution. Hence, this paper will compare the accuracy of KNN and LR models in order to find phishing websites. The major objective of this paper is to use key features to detect phishing websites with higher accuracy and also lower rate of error.
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