Cognitive radio (CR) is a revolutionary paradigm to solve the spectrum scarcity problem in wireless networks. In cognitive radio networks, cooperative spectrum sensing is regarded as a promising approach method to significantly improve the performance of spectrum sensing, but it can be threatened by Byzantine attack. The existing defense references have focused on how to mitigate the negative effect of Byzantine attack, but with some strong assumptions, such as the attackers are in minority and/or a trusted node exists for data fusion. This observation motivates us to comprehensively analyze the strategies of Byzantine attack and the fusion center (FC) in the absence of these restrictions. To be specific, we consider a generic Byzantine attack model by analyzing sophisticated malicious behaviors, which goes beyond the existing models for its generalization. Under this generalized attack model, we derive the condition which makes the FC blind from malicious perspective. On this basis, the optimal attack strategy to maximize Bayes risk is analyzed, respectively, in the case of the unknown and known fusion rule. Furthermore, we extend our analysis to the scenario where the FC has the knowledge of the attack strategy by an estimation algorithm and adopts the optimal fusion rule. Thus, we also give the closed form expression, in terms of the optimal attack strategy under different scenarios, sequentially. At last, the extensive numerical results are provided to verify our theoretical analyses and proposed estimation algorithm.

Cooperative spectrum sensing (CSS) is regarded as a promising approach to identify the available spectrum. However, it not only requires the large communication resource-consuming, but also exposes a high vulnerability to Byzantine attack. In this letter, starting with sequential voting rule (SVR), we propose a low-complexity sequential 0/1 (S0/1) for CSS in the presence of strategic Byzantine attack, which neither requires strong assumptions and any prior knowledge nor depends on careful threshold selection. Compared with existing approaches, simulation results show that S0/1 reduces the sample size in support of the higher correct sensing ratio, even in the blind scenario.

Cooperative spectrum sensing (CSS) is envisaged as a powerful approach to improve the utilization of scarce radio spectrum resources, but it is threatened by Byzantine attack. Byzantine attack has been becoming a popular research topic in both academia and industry due to the demanding requirements of security. Extensive research mainly aims at mitigating the negative effect of Byzantine attack on CSS, but with some strong assumptions, such as attackers are in minority or trusted node(s) exist for data fusion, while paying little attention to a mobile scenario. This paper focuses on the issue of designing a general and reliable reference for CSS in a mobile network. Instead of the previously simplified attack, we develop a generic Byzantine attack model from sophisticated behaviors to conduct various attack strategies and derive the condition of which Byzantine attack makes the fusion center (FC) blind. Specifically, we propose a robust sequential CSS (SCSS) against dynamic Byzantine attack. Our proposed method solves the unreliability of the FC by means of delivery-based assessment to check consistency of individual sensing report, and innovatively reuses the sensing information from Byzantines via a novel weight allocation mechanism. Furthermore, trust value (TrV) ranking is exploited to proceed with a sequential test which generates a more accurate decision about the presence of phenomenon with fewer samples. Lastly, we carry out simulations on comparison of existing data fusion technologies and SCSS under dynamic Byzantine attack, and results verify the theoretical analysis and effectiveness of our proposed approach. We also conduct numerical analyses to demonstrate explicit impacts of secondary user (SU) density and mobility on the performance of SCSS.

In cognitive radio networks (CRNs), a spectrum-sensing-data-falsification (SSDF) attack is a security issue. To deal with SSDF attacks, a support vector machine (SVM) based scheme is proposed here. Our scheme analyzes secondary user's behaviors from multi-round records of energy values, and obtains a novel evaluation index, classification accuracy. In particular, the concepts of recognition probability and misclassification probability are introduced, and the tradeoff relationship between misclassification probability and threshold of classification accuracy is theoretically obtained. Moreover, the asymptotic optimal property is derived. It enables excellent adaptability for malicious secondary user (MSU) detection in various scenarios. Exhaustive simulation results demonstrate that our proposed scheme outperforms other existing approaches.

In the face of explosive growth of wireless data and electronic devices, cognitive radio (CR) technology is an effective solution to identify spectrum resources underutilized by the primary user (PU). Moreover, cooperative spectrum sensing (CSS) is the key function of CR technology to enhance the detection performance by exploiting spatial diversity via the observations of spatially located sensor nodes (SNs). However, in a classical cooperative paradigm, each SN is always required to report individual local decision about the PU status to the fusion center (FC) for the global decision-making. Such a static reporting model regardless of the surrounding environment is not conducive to achieving the cooperative gain and adapting to a large cognitive wireless sensor network (CWSN). In view of this, this paper proposes an efficient CSS to optimize the reporting time and the achievable throughput of CWSNs, in which consists of selective reporting, multi-bit quantization, data fusion, and dynamic parameter. First of all, SNs only report some necessary observations by means of selective reporting, and then encodes local observations into multi-bit binary codes and send them to the FC. Further, the received local observations are aggregated into a final decision about the PU status. To be specific, the bit number of binary data and the selective reporting range as a parameter are dynamically adjusted at each SN. At last, simulation results demonstrate that the proposed CSS algorithm significantly not only reduces reporting time, but also improves the achievable throughput of CWSNs while maintaining the CSS performance.