Adaptive Cloud Security Framework Based On Deep Reinforcement Learning For Cyber Threat Mitigation

Abstract

The rapid adoption of cloud computing has transformed modern information technology infrastructures by offering scalable, flexible, and cost-effective services. However, this widespread adoption has also introduced significant security challenges, including data breaches, unauthorized access, and sophisticated cyberattacks. Traditional security mechanisms often struggle to cope with the dynamic and large-scale nature of cloud environments, creating a need for intelligent and adaptive defense systems. In this context, techniques from Artificial Intelligence and deep Learning have emerged as powerful tools for enhancing cloud security. To address these challenges, this paper proposes an intelligent cyber defence framework (ICDF) that leverages techniques from Artificial Intelligence and deep reinforcement learning (DRL) for enhanced cloud security. The proposed approach integrates deep reinforcement learning algorithms to enable autonomous and adaptive decision-making in threat detection and mitigation. Unlike conventional machine learning methods, reinforcement learning allows the system to learn optimal security policies through continuous interaction with the cloud environment. By modeling cybersecurity as a sequential decision-making problem, the system dynamically identifies vulnerabilities, detects anomalies, and responds to cyber threats in real time. In this framework, the deep reinforcement learning agent analyzes network traffic patterns, user behaviors, and system activities to detect malicious actions such as distributed denial-of-service (DDoS) attacks, unauthorized access, and data breaches. The agent continuously improves its performance by receiving feedback in the form of rewards and penalties, thereby optimizing defense strategies over time. This adaptive learning capability enhances the system’s ability to respond to evolving and previously unseen cyber threats. Experimental results demonstrate that the proposed AI-driven cyber defence model significantly improves detection accuracy, reduces response time, and enhances resilience against complex attacks compared to traditional security approaches. Overall, this research highlights the effectiveness of combining artificial intelligence with deep reinforcement learning to develop robust, scalable, and self-adaptive cloud security solutions.