12,796 research outputs found
Demo: closed-loop security orchestration in the telco cloud for moving target defense
This work presents a Moving Target Defense (MTD) framework for the protection of network slices and virtual resources in a telco cloud environment. The preliminary implementation provides closed-loop security management of services with proactive MTD operations to reduce the success probability of attacks, and reactive MTD operations, empowered by a tampering detection and a traffic-based anomaly detection system. MTD strategies are adaptive and optimized with deep reinforcement learning (deep-RL) for balancing costs, security, and availability goals defined in a Multi-Objective Markov Decision Process (MOMDP)
QFlip: An Adaptive Reinforcement Learning Strategy for the FlipIt Security Game
A rise in Advanced Persistent Threats (APTs) has introduced a need for
robustness against long-running, stealthy attacks which circumvent existing
cryptographic security guarantees. FlipIt is a security game that models
attacker-defender interactions in advanced scenarios such as APTs. Previous
work analyzed extensively non-adaptive strategies in FlipIt, but adaptive
strategies rise naturally in practical interactions as players receive feedback
during the game. We model the FlipIt game as a Markov Decision Process and
introduce QFlip, an adaptive strategy for FlipIt based on temporal difference
reinforcement learning. We prove theoretical results on the convergence of our
new strategy against an opponent playing with a Periodic strategy. We confirm
our analysis experimentally by extensive evaluation of QFlip against specific
opponents. QFlip converges to the optimal adaptive strategy for Periodic and
Exponential opponents using associated state spaces. Finally, we introduce a
generalized QFlip strategy with composite state space that outperforms a Greedy
strategy for several distributions including Periodic and Uniform, without
prior knowledge of the opponent's strategy. We also release an OpenAI Gym
environment for FlipIt to facilitate future research.Comment: Outstanding Student Paper awar
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