In this paper, we propose to introduce an emergent Self-Awareness (SA) module at the physical layer (PHY) in Cognitive Unmanned Aerial Vehicle (UAV) Radios to improve PHY security, especially against jamming attacks. SA is based on learning a hierarchical representation of the radio environment by means of a proposed Hierarchical Dynamic Bayesian Network (HDBN). It is shown how the acquired knowledge from previous experiences facilitate the radio spectrum perception and allow the radio to detect abnormal behaviours caused by jamming attacks. Detecting abnormalities realize a fundamental step towards growing up incrementally the radio’s long-term memory. Deviations from predictions estimated during abnormal situations are used to characterize jammers at multiple levels and discover their dynamic behavioural rules. Besides, a proactive consequence can be drawn after estimating the jammer’s signal to act efficiently by mitigating its effects on the received stimuli. Simulation results show that the introduction of the novel SA functionalities with the proposed HDBN framework provides the high accuracy of characterizing, detecting and predicting the jammer’s activities.

中文翻译：

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用于认知无人机无线电物理层安全的紧急自我意识模块


在本文中，我们建议在认知无人机（UAV）无线电的物理层（PHY）引入一种新兴的自我意识（SA）模块，以提高PHY安全性，特别是针对干扰攻击。 SA 基于通过提议的分层动态贝叶斯网络 (HDBN) 学习无线电环境的分层表示。它展示了从以前的经验中获得的知识如何促进无线电频谱感知并允许无线电检测干扰攻击引起的异常行为。检测异常是逐步增强无线电长期记忆的一个基本步骤。在异常情况下估计的预测偏差用于在多个级别上表征干扰机并发现其动态行为规则。此外，在估计干扰器的信号后，可以通过减轻其对接收到的刺激的影响来有效地采取行动，从而得出主动的结果。仿真结果表明，通过所提出的 HDBN 框架引入新颖的 SA 功能，可以高精度地表征、检测和预测干扰机的活动。