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.

本文提出了一种基于时间模式的背门攻击方法，通过一系列的时间限制来控制背门行为的发动和持续时间，实现在云计算中调度任务过程中的达成攻击目的和保持攻击效果和隐秘性，实验结果表明，该背门攻击方法可以达到很高的攻击成功率和干扰效果。