Motivated by recent developments in full-duplex radios, cognitive radios, and data-driven signal-processing, we propose a novel class of reactive jamming adversaries wherein the adversary transmits jamming energy on the victim's frequency band while simultaneously monitoring various energy statistics in the network to detect the presence of potential countermeasures, thereby trapping the victim. These adversaries employ generalized energy detectors comprising statistical detectors, based on instantaneous and distributional energy metrics, and data-driven detectors employing machine-learning classifiers to learn patterns in the observed energy sequences. Against such a strong adversary, we propose a family of cooperative mitigation strategies wherein the victim takes assistance from a helper node, with the strategies tailored to operate under a wide range of latency requirements on victim's messages and practical radio hardware constraints at helper node. To provide theoretical guarantees on their efficacy, interesting optimization problems are formulated on the choice of their underlying parameters, followed by extensive mathematical analyses on their error performance and covertness. To assess their practical feasibility, we implement the before-deployment and after-deployment setups on a software-defined-radio-based hardware testbed, and to evaluate their detectability on real energy observations, we collect the corresponding datasets to train and test the data-driven machine-learning classifiers employed by adversary. Both analytical and hardware evaluations show that the proposed strategies cannot be detected with a high-probability under the generalized energy detectors used by adversary.

翻译：受全双工无线电、认知无线电以及数据驱动信号处理领域最新进展的启发，我们提出了一类新型反应式干扰攻击者，该攻击者在受害者频段发射干扰能量的同时，持续监测网络中的各类能量统计信息以检测潜在对抗措施的存在，从而压制受害者。此类攻击者采用包含统计检测器与数据驱动检测器的广义能量检测方案：前者基于瞬时能量指标与分布能量指标，后者则通过机器学习分类器学习所观测能量序列中的模式。针对此类强攻击者，我们提出了一系列协同缓解策略，其中受害者借助辅助节点实施对抗，这些策略能够适应受害者消息传输对时延的不同要求以及辅助节点实际无线电硬件约束。为保障策略效力的理论可靠性，我们围绕核心参数选择建立了优化问题模型，并通过大量数学分析评估了其错误性能与隐蔽性。为验证实际可行性，我们在基于软件定义无线电的硬件测试平台上分别实现了部署前与部署后两种实验配置；同时，为评估真实能量观测下的可检测性，我们采集了相应数据集用于训练与测试攻击者采用的数据驱动机器学习分类器。理论分析与硬件评估均表明，所提策略在攻击者采用的广义能量检测器下能够以高概率避免被检测。