Mitigating Cross-Site Request Forgery (CSRF) Attacks Using Reinforcement Learning and Predictive Analytics

Abstract

Cross-Site Request Forgery (CSRF) attacks pose a significant threat to web application security, allowing attackers to perform unauthorized actions on behalf of authenticated users. Traditional CSRF mitigation techniques, such as using secure tokens and validating request origins, have limitations in adapting to attack patterns and optimizing security policies. This research explores the application of reinforcement learning (RL) and predictive analytics to enhance CSRF mitigation strategies. We propose several RL-based approaches, including CSRF token generation, CSRF detection, request validation, user behavior analysis, and security policy optimization. In these approaches, RL agents are trained to generate secure tokens, detect CSRF attacks, validate request authenticity, model user behavior, and optimize security policies based on observed attack patterns and system performance. The agents learn through simulated attack scenarios, real-world web traffic data, and continuous feedback, adapting to new CSRF techniques and balancing security effectiveness with user experience. Additionally, we investigate predictive analytics techniques for CSRF mitigation, such as anomaly detection, risk scoring, user behavior analysis, predictive token generation, and adaptive security policies. These techniques leverage machine learning algorithms to identify anomalous requests, assign risk scores, classify user behavior, generate secure tokens, and dynamically adjust security measures based on predicted risk levels. The research demonstrates the applications of RL and predictive analytics in enhancing CSRF mitigation strategies. These approaches offer promising solutions to strengthen web application security by proactively detecting and preventing CSRF attacks, adapting to attack patterns, and optimizing security policies. Further research is needed to validate the practicality and scalability of these techniques in real-world deployments and to integrate them with existing CSRF mitigation best practices. This research contributes to the field of web application security by introducing innovative approaches that leverage RL and predictive analytics to mitigate CSRF attacks. The proposed techniques may significantly improve the resilience of web applications against CSRF threats.