Phishing Detection in Browser-in-the-Middle: A Novel Empirical Approach Incorporating Machine Learning Algorithms

BitM (Browser-in-the-middle) is a new generation phishing that has a high probabilistic rate to infringe present-day two or multifactor authentication logins which are said to be protected. In response to counter this security threat an internally curated dataset is generated to facilitate the detection of BitM within the context of phishing. As the existing approaches emphasize the mitigation of BitM phishing instead of its detection, this study introduces a novel empirical approach that utilizes data packets incorporating machine learning algorithms for the detection of BitM phishing attacks. The limited availability of training datasets due to inadequate BitM testing facilities restricted the expansion of our dataset. This study is inspired by the High severity score of the BitM attack according to Common Attack Pattern Enumeration and Classification (CAPEC). The accuracies of five classifiers being used namely SVM, MLP, Naive Bayes, Random Forest, and Decision Tree, are examined, with Random Forest having the highest performance of 99.1% accuracy.