Reversed Forward Feature Selection Algorithm for Website Fingerprinting Attacks Mitigation

Due to the vast and fast growth of the internet, privacy is becoming a concern. Web browser data privacy is a part of the overall users' privacy. Since the attackers took social engineering to the next level, achieving the web browser history data secured from privacy breaches becomes difficult. In this work, we implemented three types of web fingerprinting attack using ML algorithms (DT_Attack, RF_Attack and AdaB_Attack) with the best performance results (Accuracy 99.9%, precision 100%, recall 100% and execution time 339 seconds) achieved using the Decision Tree classifier (DT_Attack). Then we proposed a defense mechanism using the forward feature selection algorithm named Reverse Forward Feature selection, which contributes to reducing the efficiency of the previously implemented web fingerprinting attacks. More specifically, accuracy, precision, and recall of the implemented attacks were reduced on all attack types between 44.8-54.3%, 24.3-38.6%, 53.6-70.2% respectively, without adding any overhead in terms of bandwidth and execution time.