This paper presents a novel probabilistic detection scheme called Cooperative Statistical Detection~(CSD) for abnormal node detection while defending against adversarial attacks in cluster-tree wireless sensor networks. The CSD performs a two-phase process: 1) designing a likelihood ratio test~(LRT) for a non-root node at its children from the perspective of packet loss; 2) making an overall decision at the root node based on the aggregated detection data of the nodes over tree branches. In most adversarial scenarios, malicious children knowing the detection policy can generate falsified data to protect the abnormal parent from being detected. To resolve this issue, a mechanism is presented in the CSD to remove untrustworthy information. Through theoretical analysis, we show that the LRT-based method achieves the perfect detection. Furthermore, the optimal removal threshold is derived for falsifications with uncertain strategies and guarantees perfect detection of the CSD. As our simulation results shown, the CSD approach is robust to falsifications and can rapidly reach $99\%$ detection accuracy, even in existing adversarial scenarios, which outperforms the state-of-the-art technology.

翻译：本文提出了一种新颖的概率检测方案，称为协作式统计检测（CSD），用于在簇树无线传感器网络中检测异常节点并防御对抗性攻击。CSD执行两阶段过程：1）从数据包丢失的角度，为非根节点在其子节点处设计似然比检验（LRT）；2）在根节点处基于树分支上节点的聚合检测数据做出整体决策。在大多数对抗性场景中，了解检测策略的恶意子节点可能生成伪造数据以保护异常父节点不被检测。为解决此问题，CSD中提出了一种机制来移除不可信信息。通过理论分析，我们证明了基于LRT的方法可实现完美检测。此外，针对策略不确定的伪造行为，推导了最优移除阈值，并保证了CSD的完美检测能力。如我们的仿真结果所示，CSD方法对伪造行为具有鲁棒性，即使在现有对抗性场景下也能快速达到$99\%$的检测准确率，其性能优于现有最先进技术。