CSLE: A Reinforcement Learning Platform for Autonomous Security Management
Reinforcement learning is a promising approach to autonomous and adaptive security management in networked systems. However, current reinforcement learning solutions for security management are mostly limited to simulation environments and it is unclear how they generalize to operational systems. In this paper, we address this limitation by presenting CSLE: a reinforcement learning platform for autonomous security management that enables experimentation under realistic conditions. Conceptually, CSLE encompasses two systems. First, it includes an emulation system that replicates key components of the target system in a virtualized environment. We use this system to gather measurements and logs, based on which we identify a system model, such as a Markov decision process. Second, it includes a simulation system where security strategies are efficiently learned through simulations of the system model. The learned strategies are then evaluated and refined in the emulation system to close the gap between theoretical and operational performance. We demonstrate CSLE through four use cases: flow control, replication control, segmentation control, and recovery control. Through these use cases, we show that CSLE enables near-optimal security management in an environment that approximates an operational system.
Code (0)
등록된 구현이 없습니다.
Tasks
Reinforcement LearningSimilar Papers 제목 키워드 기반
AcrosticSleuth: Probabilistic Identification and Ranking of Acrostics in Multilingual Corpora
For centuries, writers have hidden messages in their texts as acrostics, where initial letters of consecutive lines or paragraphs form meaningful words or phrases. Scholars searching for acrostics manually can only focus…
Binary ClassificationDecentralized autonomous organization and blockchain-based incentivization framework for community-based facilities management
Traditional facility management often relies on centralized decision-making structures that limit stakeholder participation, leading to misalignment with occupant needs and reduced satisfaction. This paper proposes a nov…
Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond
Fined-grained anomalous cell detection from affected tissues is critical for clinical diagnosis and pathological research. Single-cell sequencing data provide unprecedented opportunities for this task. However, current a…
Anomaly DetectionCell DetectionDomain AdaptationBEAR: Physics-Principled Building Environment for Control and Reinforcement Learning
Recent advancements in reinforcement learning algorithms have opened doors for researchers to operate and optimize building energy management systems autonomously. However, the lack of an easily configurable building dyn…
energy managementManagementModel Predictive Controlreinforcement-learning+2A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learning
Autonomous intersection management (AIM) poses significant challenges due to the intricate nature of real-world traffic scenarios and the need for a highly expensive centralised server in charge of simultaneously control…
Autonomous VehiclesManagementMulti-agent Reinforcement LearningNavigate