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- W4378587130 abstract "We propose an Efficient moVing tArget DEfense (EVADE) that periodically changes a network topology to thwart potential attackers for protecting a given network. To achieve autonomous network topology adaptations under high dynamics, we leverage deep reinforcement learning (DRL) in a moving target defense (MTD) strategy to defeat epidemic attacks. EVADE has two objectives, minimizing security vulnerability caused by the software monoculture and maximizing network connectivity for seamless communications. We design EVADE to autonomously shuffle a network topology by identifying a pair of network adaptation budgets to add and remove edges for generating a robust and connected network topology. To improve the learning convergence speed: 1) We propose a vulnerability ranking algorithm of edges and nodes (VREN) to effectively direct the DRL agent to select adaptations; 2) We develop a Fractal-based Solution Search (FSS) to build an efficient sampling environment for the agent to quickly converge to an optimal solution; and 3) We design density optimization (DO)-based greedy MTD to further refine the solution search space. This hybrid approach achieves faster training allowing running the DRL agent online. Via our extensive experiments under both real and synthetic networks, we demonstrate the outperformance of EVADE over its counterparts." @default.
- W4378587130 created "2023-05-28" @default.
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- W4378587130 date "2023-01-01" @default.
- W4378587130 modified "2023-09-30" @default.
- W4378587130 title "EVADE: Efficient Moving Target Defense for Autonomous Network Topology Shuffling Using Deep Reinforcement Learning" @default.
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- W4378587130 doi "https://doi.org/10.1007/978-3-031-33488-7_21" @default.
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