Reinforcement Learning-Driven Adaptation Chains: A Robust Framework for Multi-Cloud Workflow Security

2025-01-10

AI Summary

To address delayed response to dynamic security threats, conflicting multi-objective optimization, and high uncertainty in adaptation costs in multi-cloud workflows, this paper proposes an adaptive security chain mechanism based on reinforcement learning (specifically PPO and DQN). The method innovatively integrates control- and data-dependency modeling into multi-step, coordinated remediation decisions, generating cost-aware repair action sequences guided by user requirements, attack characteristics, and task dependencies. Execution and evaluation are realized via a closed-loop integration of the jBPM workflow engine and the CloudSim Plus simulation platform. Experimental results demonstrate that, compared to single-step adaptation approaches, the proposed mechanism significantly reduces total adaptation cost while exhibiting superior resilience and robustness—evidenced by lower latency, reduced data leakage risk, and decreased SLA violation rate.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Cloud computing has emerged as a crucial solution for managing data- and compute-intensive workflows, offering scalability to address dynamic demands. However, security concerns persist, especially for workflows involving sensitive data and tasks. One of the main gaps in the literature is the lack of robust and flexible measures for reacting to these security violations. To address this, we propose an innovative approach leveraging Reinforcement Learning (RL) to formulate adaptation chains, responding effectively to security violations within cloud-based workflows. These chains consist of sequences of adaptation actions tailored to attack characteristics, workflow dependencies, and user-defined requirements. Unlike conventional single-task adaptations, adaptation chains provide a comprehensive mitigation strategy by taking into account both control and data dependencies between tasks, thereby accommodating conflicting objectives effectively. Moreover, our RL-based approach uses insights from past responses to mitigate uncertainties associated with adaptation costs. We evaluate the method using our jBPM and Cloudsim Plus based implementation and compare the impact of selected adaptation chains on workflows with the single adaptation approach. Results demonstrate that the adaptation chain approach outperforms in terms of total adaptation cost, offering resilience and adaptability against security threats.

Problem

• Cloud Computing
• Security Issues
• Adaptive Solutions

Innovation

• Reinforcement Learning
• Adaptive Chain Model
• Cloud Security