Defending Web Applications against Malicious Traffic: Leveraging Machine Learning for Enhanced Security

As the frequency and complexity of cybersecurity threats continue to rise, it is imperative to develop novel strategies for preventing and detecting malicious attacks. This paper proposes an innovative approach that combines customized pre-processing techniques and machine learning algorithms to identify and mitigate attacks utilizing malicious user agents and payloads in a reverse proxy. The proposed system comprises two distinct machine learning models: one designed to detect suspicious user agents, and another focused on identifying malicious payloads. By implementing a customized pre-processing step, the raw data is cleansed, relevant features are extracted, and noise is reduced. The refined data is then utilized to train the machine learning models, which are seamlessly integrated into a reverse proxy. This integration enables the identification and blocking of incoming traffic that exhibits characteristics akin to the identified malicious data. Moreover, the system categorizes the type of attack based on the detected malicious user agent and payload. To evaluate the proposed system, a comprehensive dataset of real-world attacks is employed, demonstrating the high accuracy and effectiveness of the customized pre-processing and machine learning-based approach in detecting and preventing malicious traffic within a reverse proxy. As an integral component of a comprehensive cybersecurity solution, this system augments the security of web applications and fortifies defenses against a wide array of attack vectors.