In wireless security, cognitive adversaries are known to inject jamming energy on the victim's frequency band and monitor the same band for countermeasures thereby trapping the victim. Under the class of cognitive adversaries, we propose a new threat model wherein the adversary, upon executing the jamming attack, measures the long-term statistic of Kullback-Leibler Divergence (KLD) between its observations over each of the network frequencies before and after the jamming attack. To mitigate this adversary, we propose a new cooperative strategy wherein the victim takes the assistance for a helper node in the network to reliably communicate its message to the destination. The underlying idea is to appropriately split their energy and time resources such that their messages are reliably communicated without disturbing the statistical distribution of the samples in the network. We present rigorous analyses on the reliability and the covertness metrics at the destination and the adversary, respectively, and then synthesize tractable algorithms to obtain near-optimal division of resources between the victim and the helper. Finally, we show that the obtained near-optimal division of energy facilitates in deceiving the adversary with a KLD estimator.

翻译：在无线安全领域，认知对抗者已知会向受害者的频带注入干扰能量，并监听同一频带以探测反制措施，从而困住受害者。针对认知对抗者这一类敌人，我们提出了一种新的威胁模型：在该模型中，对抗者在执行干扰攻击后，会测量网络各频率上其在攻击前后观测数据的长期统计量——库尔贝克-莱布勒散度（KLD）。为缓解这种对抗，我们提出了一种新的协作策略：受害者借助网络中的辅助节点实现可靠通信，将信息传送至目的地。其核心思想是合理分配双方的能量与时间资源，使得信息在可靠传输的同时，不破坏网络中采样样本的统计分布。我们分别对通信的可靠性（目的地端）与隐蔽性（对抗者端）进行了严格分析，并推导出可求解的算法，以获取受害者和辅助节点之间近乎最优的资源分配方案。最后，我们证明该近乎最优的能量分配能够有效欺骗使用KLD估计器的对抗者。