论文标题
对深度强化学习的颞pattern后门攻击
A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning
论文作者
论文摘要
深度强化学习（DRL）在许多现实世界应用中取得了重大成就。但是，这些现实世界的应用通常只能提供部分观察，以便由于遮挡和嘈杂的传感器而做出决策。但是，部分状态可观察性可用于隐藏后门的恶意行为。在本文中，我们探讨了DRL的顺序性质，并提出了对DRL的新型时间图案后门攻击，DRL的触发是对一系列观测值而不是单个观察的一组时间约束，并且可以在可控的持续时间内保持效果，而不是在瞬间。我们将提出的后门攻击验证为云计算中典型的作业调度任务。许多实验结果表明，我们的后门可以实现出色的有效性，隐形和可持续性。我们的后门的平均清洁数据准确性和攻击成功率分别可以达到97.8％和97.5％。
Deep reinforcement learning (DRL) has made significant achievements in many real-world applications. But these real-world applications typically can only provide partial observations for making decisions due to occlusions and noisy sensors. However, partial state observability can be used to hide malicious behaviors for backdoors. In this paper, we explore the sequential nature of DRL and propose a novel temporal-pattern backdoor attack to DRL, whose trigger is a set of temporal constraints on a sequence of observations rather than a single observation, and effect can be kept in a controllable duration rather than in the instant. We validate our proposed backdoor attack to a typical job scheduling task in cloud computing. Numerous experimental results show that our backdoor can achieve excellent effectiveness, stealthiness, and sustainability. Our backdoor's average clean data accuracy and attack success rate can reach 97.8% and 97.5%, respectively.
