This paper investigates attack- and channel-aware decision fusion for Reconfigurable Intelligent Surface (RIS)-enhanced Cooperative Spectrum Sensing (CSS) in Cognitive Radio Networks (CRNs) to mitigate the challenge from Byzantine attacks. Specifically, we first propose the optimal hard decision fusion rule for the Fusion Center (FC) based on maximum-likelihood criterion, which simultaneously accounts for channel impairments and statistical characteristics of Byzantine attacks. Following from this result, we then derive three suboptimal and low-complexity decision fusion rules when the Channel State Information (CSI) cannot be perfectly achieved at the FC. The correspondingly results indicate that negative weighting coefficients can be adaptively assigned to malicious reports based on attack intensity, which can successfully transform adversarial interference into effective detection gains for the FC in some scenarios. This finding profoundly reveals the intrinsic mechanism of how Byzantine attacks impact the decision fusion, and thus provide a rigorous theoretical perspective for developing robust decision fusion rule capable of adaptively suppressing and conversely exploiting malicious reports. Furthermore, to make practical implementation of our decision fusion rules, we develop simple and unbiased attack parameter estimation algorithms based on the first-order statistics of received reports at the FC, which also exhibits good convergence. Our results indicate that we can insert a virtual source under control, and send false data to the Byzantine attackers. This deception strategy can help the FC successfully learn the attack parameter aided by its collected data. Finally, extensive simulations are conducted and the correspondingly results demonstrate that our proposed fusion rules can effectively mitigate Byzantine attacks across a wide range of attack scenarios, and they can outperform traditional malicious report filtering defense algorithm by successfully reversing and exploiting malicious reports.

From the perspective of hard decision fusion, we investigate Byzantine attacks in Reconfigurable Intelligent Surface (RIS)-enhanced and decode-and-forward relay-assisted Cooperative Spectrum Sensing (CSS) for mobile Cognitive Radio Networks (CRNs) in this paper. Specially, a RIS-enhanced and decode-and-forward relay-assisted CSS configuration is first constructed under dynamic channel scenarios due to user mobility. Subsequently, the channel- and attack-aware hard decision fusion rules are developed, and the optimal channel-aware Byzantine attack strategies are then developed under both small-scale and large-scale attacking scenarios. The corresponding results depict that the optimal attack strategy does not require any a prior knowledge of the global instantaneous Channel State Information (ICSI) (e.g. false alarm probability and detection probability of all the secondary users), although perfect acquisition of ICSI is clearly always not affordable from the attacker perspective, which is further exacerbated by the RIS and decode-and-forward relays involved in CSS and the potential high mobility of secondary users that leads to fast fading channels. Furthermore, our counterintuitive results also indicate that, regardless of the attacker's awareness of the decision fusion rule, the optimal Byzantine attack can be achieved through a unifying framework, the explicit attack strategy may be not unique, and the attacking effectiveness is primarily determined by the fraction of the Byzantine nodes rather than the channel dynamics. That is, to make the channel-aware approach more practical, the challenge that the heavy reliance on the global ICSI and decision fusion rule in obtaining the Byzantine attacks is successfully relaxed. Finally, we empirically validate our theoretical analysis through extensive simulations across a wide range of attacking scenarios.