An improved anti‐phishing model utilizing TF‐IDF and AdaBoost

Abstract

SummaryA behavior over the internet, which results into false utilization of the resources is termed as phishing and the techniques to identify them are termed as anti‐phishing frames and algorithms. This research work introduces a new relative similarity approach utilizing similarity index co‐relation by applying Cosine similarity. In order to incorporate the co‐relation, term frequency and inverse document frequency (TF‐IDF) is also calculated. AdaBoost is employed to improve the classification accuracy of the proposed work by means of enhancing the performance of weak learner to classify website into legitimate and phishing one. The simulation analysis has shown that the proposed work outperformed the existing works in terms of accuracy, TPR, and precision of detecting phishing sites to offer secure browsing. Overall model evaluation against 50,000 URL instances have demonstrated an average precision, TPR, F‐measure, accuracy and execution time of 98.64%, 96.52%, 97.56%, 98.014%, and 3.485 s, respectively for phishing detection. This proves the success of the proposed model in shielding phishing attack.