Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning

2025-05-13

AI Summary

To address the ineffectiveness of static security policies in cloud environments—caused by dynamic threats and elastic resource scaling—this paper proposes an adaptive security policy management framework based on deep reinforcement learning. The method uniquely integrates Deep Q-Networks (DQN) with Proximal Policy Optimization (PPO) to enable real-time, coordinated optimization of firewall rules and Identity and Access Management (IAM) policies. It jointly optimizes security effectiveness, response latency, and system overhead under strict compliance and minimal-resource-perturbation constraints. The framework incorporates AWS CloudTrail log analysis, network traffic modeling, and multi-source threat intelligence. Experimental evaluation demonstrates a 10-percentage-point improvement in intrusion detection rate (reaching 92%), a 58% reduction in average incident response time, and validates the framework’s efficacy and practicality in a production AWS environment.

Technology Category

• Application Domains: Security
• Search and Optimization: Learning to Search
• Machine Learning: Hardware-aware ML

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The security of cloud environments, such as Amazon Web Services (AWS), is complex and dynamic. Static security policies have become inadequate as threats evolve and cloud resources exhibit elasticity [1]. This paper addresses the limitations of static policies by proposing a security policy management framework that uses reinforcement learning (RL) to adapt dynamically. Specifically, we employ deep reinforcement learning algorithms, including deep Q Networks and proximal policy optimization, enabling the learning and continuous adjustment of controls such as firewall rules and Identity and Access Management (IAM) policies. The proposed RL based solution leverages cloud telemetry data (AWS Cloud Trail logs, network traffic data, threat intelligence feeds) to continuously refine security policies, maximizing threat mitigation, and compliance while minimizing resource impact. Experimental results demonstrate that our adaptive RL based framework significantly outperforms static policies, achieving higher intrusion detection rates (92% compared to 82% for static policies) and substantially reducing incident detection and response times by 58%. In addition, it maintains high conformity with security requirements and efficient resource usage. These findings validate the effectiveness of adaptive reinforcement learning approaches in improving cloud security policy management.

Problem

• Dynamic security policy adaptation in cloud environments
• Overcoming limitations of static policies using reinforcement learning
• Improving intrusion detection and response times in AWS

Innovation

• Uses reinforcement learning for dynamic security policies
• Leverages deep Q Networks and proximal optimization
• Integrates cloud telemetry for continuous policy refinement