MASD: Malicious Web Session Detection Using ML-Based Classifier

The development of web applications and services has resulted in an increase in security concerns, especially in identifying malicious web session attacks. Malicious web sessions pose a significant risk to users, potentially resulting in data breaches, illegal access, and other malicious activities. This study presents an innovative technique for detecting malicious web sessions using a machine learning-driven classifier. To examine the features of web sessions, the suggested technique combines an embedding layer and machine learning approaches. Three different datasets were used in the empirical studies to confirm the effectiveness of the approach. They include a unique compilation of Internet banking web request logs, provided by Yap Kredi Teknoloji, as well as the well-known HTTP dataset CSIC 2010 and the publicly accessible WAF dataset. The experimental results are compared to known approaches such as Random Forest, Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Naïve Bayes, Decision Trees, DBSCAN, and Self-Organizing Maps (SOM). The actual findings demonstrate the superiority of the suggested technique, especially when Random Forest is used as the chosen classifier. The attained accuracy rate of 99.17% surpasses the comparison methodologies, highlighting the approach’s ability to efficiently identify and block malicious web sessions.