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Online learning-based optimal primary user emulation attacks in cognitive radio networks

机译：认知无线电网络中基于在线学习的最佳主用户仿真攻击

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In a cognitive radio (CR) network, a secondary user learns the spectrum environment and dynamically accesses the channel where the primary user is inactive. At the same time, a primary user emulation (PUE) attacker can send falsified primary user signals and prevent the secondary user from utilizing the available channel. Although there is a large body of work on PUE attack detection and defending strategies, the best attacking strategies that an attacker can apply have not been well studied. In this paper, for the first time, we study the optimal PUE attack strategies without any prior knowledge on the primary user activity characteristics and the secondary user access strategies. We formulate the problem as a non-stochastic online learning problem where the attacker needs to dynamically decide the attacking channel in each time slot based on its attacking experience in previous slots. The challenge in our problem is that the PUE attacker cannot observe the reward on the attacked channel because it never knows if a secondary user ever tries to access it. To solve this challenge, we propose an attack-but-observe-another (ABOA) scheme, in which the attacker attacks one channel in the spectrum sensing phase, but observes at least one other channel in the data transmission phase. We propose two non-stochastic online learning-based attacking algorithms, EXP3-DO and OPT-RO, which select the observing channel deterministically based on the attacking channel and uniform randomly, respectively. EXP3-DO employs an existing theoretical framework and is suboptimal. OPT-RO is based on the new proposed theoretical framework and is optimal. They achieve regret in the order of O(T2/3) and O(√T), respectively. T is the number of slots the CR network operates. We also generalize OPT-RO to multichannel observation cases. We show consistency between simulation and analytical results under various system parameters.
机译：在认知无线电（CR）网络中，辅助用户学习频谱环境并动态访问主要用户处于非活动状态的信道。同时，主要用户仿真（PUE）攻击者可以发送伪造的主要用户信号，并阻止次要用户利用可用通道。尽管在PUE攻击检测和防御策略方面有大量工作要做，但是对于攻击者可以采用的最佳攻击策略，还没有进行充分的研究。在本文中，我们是第一次研究最佳的PUE攻击策略，而没有对主要用户活动特征和次要用户访问策略的任何先验知识。我们将此问题表述为非随机的在线学习问题，攻击者需要根据其在先前时隙中的攻击经验，在每个时隙中动态决定攻击通道。我们这个问题的挑战在于，PUE攻击者无法在被攻击的频道上观察到奖励，因为它永远不知道辅助用户是否曾经尝试访问该奖励。为了解决这一挑战，我们提出了一种“攻击但另一观察”（ABOA）方案，其中，攻击者在频谱感知阶段攻击一个信道，但在数据传输阶段观察至少另一个信道。我们提出了两种基于在线学习的非随机攻击算法：EXP3-DO和OPT-RO，它们分别基于攻击通道确定地选择观察通道，并随机选择观察通道。 EXP3-DO采用了现有的理论框架，并且是次优的。 OPT-RO基于新提出的理论框架，并且是最佳的。他们分别以O（T2 / 3）和O（√T）的顺序感到遗憾。 T是CR网络运行的插槽数。我们还将OPT-RO推广到多通道观测案例。我们展示了在各种系统参数下仿真和分析结果之间的一致性。

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