抗SSDF攻击的EWSPRT协作频谱感知方案

比较了认知无线电网络中几种典型的协作感知方案在篡改感知数据(SSDF)攻击条件下的感知性能,并提出了一种增强型加权序贯检测(EWSPRT)协作方案.在该方案中,各次用户首先通过能量检测得到2 bit本地决策,然后收集其它协作用户的感知结果进行决策融合和最终判决,并依据历史观测信息动态更新各协作用户的融合权重;利用改进的...     

利用SSDF攻击特征的加权分布式频谱感知算法

协作频谱感知可以提高频谱感知的可靠性,但易遭到篡改感知数据(Spectrum Sensing Data Falsification,SSDF)攻击。该文利用SSDF攻击特征,判断邻居节点发送值是否是恶意状态值,并提出一种加权分布式协作频谱感知算法。该算法根据状态值在本地节点网络中的偏离程度,设定其融合权值。仿真结果表明,所提算法在节点收敛率和鲁棒性两方面,比基于梯度的协作频谱感知算法和基于最大差值的协作频谱感知算法都有所提升,检测性能也因此显著提高。      

抗SSDF攻击的一致性协作频谱感知方案

在分布式认知无线电网络中,一般很难找到合适的融合中心能够收集所有协作用户的感知信息,而且协作过程极可能遭到篡改感知数据(Spectrum Sensing Data Falsification,SSDF)攻击.鉴于此,该文提出了一种改进的一致性协作频谱感知方案.利用Metropolis迭代规则,各次用户仅依靠邻接点之间的...     

基于SNR比较的协作频谱感知研究

频谱感知是认知无线电中通过发现可用频率资源提高频谱利用率的关键技术。协作频谱感知能一定程度上消除多径和阴影的影响,为了进一步提高频谱检测性能,提出了一种基于SNR比较的协作频谱感知算法。在此算法中,各个认知用户将各自的本地判决结果和自身的SNR值发送到数据融合中心,通过SNR比较,对各CR用户进行筛选,选出符合算法要求的CR用户参加协作检测,进行数据融合。计算机仿真表明,该算法能有效提高检测性能,并减少判决融合的节点数量。     

认知网络中的协作频谱感知技术研究

认知无线电技术通过感知频谱后进行动态频谱资源分配,极大地提高了频谱利用效率。频谱感知是实现认知无线电技术的前提和基础,文中针对频谱感知技术中单节点检测的局限性对能够改善频谱感知能力的协作感知技术进行了分析及讨论,重点分析了基于能量检测法的协作感知中的各种数据融合算法,并在此基础上提出了目前协作感知中存在的问题及未来的研究方向。     

协作频谱感知中的动态信任评估机制

针对动态性SSDF攻击对协作频谱感知技术的危害性,实时观察分析认知用户的感知行为,提出一种动态信任评估机制.根据认知用户的历史感知变化情况,引入时间衰减因子、加性奖励因子和加性惩罚因子,计算出随时间和协作次数不断变化的动态信任值,选取可信认知用户参与协作感知,提高了数据融合中心的协作判决准确性.仿真结果表明,动态信任机制可以有效抑制恶意用户的信任值增长,降低对“OR”、“AND”和“Majority”3种常用协作频谱感知数据融合准则的攻击成功率.     

可信的反馈声誉保护协作频谱感知软判决算法

协作频谱感知中信任机制的引入,起到了抑制恶意用户频谱感知数据伪造( SSDF)攻击行为的作用。然而,数据融合中心不加区分地接收协作感知结束后的反馈信息,为恶意用户带来了实施“掺沙子”攻击的机会。恶意用户向数据融合中心反馈错误的主用户频谱状态,使信任机制不能得出准确的信任值。为此,提出了一种基于反馈声誉的信任机制,考虑反馈中的个体性特征,引入反馈声誉的思想来量化认知用户信任值。同时,将信任值量化结果用于权重经典软判决算法———序贯概率比检测( SPRT)算法,消除SSDF恶意用户参与软判决数据融合的影响,形成可信序贯概率比检测算法( FSPRT)。仿真结果表明FSPRT算法的性能优于传统SPRT算法,能有效降低网络信任值计算误差,并保持较好的感知性能。     

利用分组过滤加强SSDF攻击防御的方案设计

在协作频谱感知网络中,设备故障、信道阴影衰落和噪声等会导致频谱感知器(如手机、平板等)发送的信息不可靠,而恶意用户在协作频谱感知网络中,也会发送错误的感知信息以混淆视听,干扰诚实用户的判决结果。不可靠消息在邻居用户间的传递必将导致感知结果产生偏差和错误,大大降低了协作频谱感知的效率。为解决上述问题,本文将置信传播算法和信誉模型相结合,提出一种基于次用户分组的频谱感知数据伪造(SSDF,Spectrum Sensing Data Falsification)攻击防御方案。该方案分两个阶段对不可靠信息进行过滤:首先,在频谱感知阶段,通过置信传播算法对次用户进行分组,过滤掉因设备故障等因素产生的不可靠用户,剩余用户则视为正常工作用户进行数据融合。然后,在数据融合阶段,根据以信誉值作为权重因子的置信传播算法来计算最终的判决值。本文所提方案分别在感知阶段和融合阶段采取了防御措施,可有效地过滤网络中的不可靠信息,减小恶劣的频谱环境对次用户感知结果的影响。仿真结果表明,本文所提方案迭代次数少、收敛快,有效地减弱了SSDF攻击带来的损害,提高了感知结果的准确性、增强了认知无线网络的安全性。      

认知无线电最优线性协作宽带频谱感知

本文提出了基于最优线性协作的宽带频谱感知方案。通过次级用户之间的协作,为认知网络的频谱感知提供分集,利用融合中心融合多个次级用户的宽带频谱感知数据来获取最优权重,并生成全局判决统计量,最终使用全局阈值完成最后的检测判决。由于所提出的两种协作宽带感知方案或需要精确地估计授权用户的信号强度和噪声方差,或感知性能不足,因而,还提出一种更易实现的方案。理论分析和仿真结果表明,本文所提出的协作感知方案可以有效地提高频谱感知性能,并且性能优于传统等增益合并方案。      

基于双重信誉值与多角度权值的抗SSDF攻击协作频谱感知方法

为保证协作频谱感知具备较好的抗频谱感知数据篡改（SSDF）攻击能力,提出一种基于双重信誉值与多角度权值的协作频谱感知（DRMW-CSS）方法。首先,以历史本地判决结果进行多次迭代获得评分信誉值,并在此基础上计算出准确率信誉值。其次,以双重信誉值和多个信誉值门限对次级用户（SU）进行筛选。然后,判断SSDF攻击对该筛选方法造成的影响程度,并将其分为3种情况。最后,根据不同情况采用相对应多角度权值数据融合判决方法做出最终的全局决策。仿真结果表明,所提方法在面对不同攻击策略与攻击概率的SSDF攻击时具有良好的感知准确率,相较于传统方法具备更好的抗攻击能力。     

Cooperative blind spectrum sensing using autocorrelation matrix

To improve the spectrum sensing performance in cognitive radios, a scheme of cooperative blind spectrum sensing based on autocorrelation matrix is proposed. The test statistic is extracted from the autocorrelation matrix of the received signal samples and a bi-threshold hybrid decision scheme is designed for local spectrum sensing. The cognitive radio base station makes a credibility fusion based on the local soft decisions and then takes global fusion combining with the local hard decisions. The proposed method is blind in the sense since it requires no apriori knowledge of the signal and the noise power. Theoretical analysis and computer simulation results show that the proposed method can enhance the spectrum sensing capability.     

一种基于信誉度的航空无线电协作频谱感知方法

摘要：传统的航空无线电协作频谱感知算法无法区分节点的性质（普通/恶意）,而新的加权序贯检测（weighted sequential probability ratio test,WSPRT）算法虽然解决了这个问题,但在具有频谱感知数据篡改（spectrum sensing data falsification,SSDF）攻击节点的环境中,无法保持高的感知正确率。提出了一种改进型WSPRT 算法,在传统的 WSPRT 算法基础上改进了信誉度奖惩方案,增加了临近时间内感知稳定度的量化。从实验仿真结果看,改进后的算法不仅时间复杂度更低,而且能够有效地识别恶意节点,对于恶意用户的判定更准确。     

认知无线电中基于自相关矩阵的协作盲感知

为了克服传统的认知无线电频谱感知算法存在的不足,提出了一种基于自相关矩阵的协作式盲频谱感知方法,以提高频谱感知能力.该方法从样本自相关矩阵中提取检测统计量,采用双门限混合判决方法得到本地检测结果.认知基站将本地检测信息分成两类,并对软判决信息实施可靠性融合,最后给出全局判决结果.该方法是一种盲感知方法,不需要知道主用户...     

CoPD: a conjugate prior based detection scheme to countermeasure spectrum sensing data falsification attacks in cognitive radio networks

Cognitive radio networks are a promising solution to the spectrum scarcity issue. In cognitive radio networks, cooperative spectrum sensing is critical to accurately detect the existence of a primary user (PU) signal, because the local spectrum sensing by a single secondary user (SU) has low reliability. Unfortunately, cooperative spectrum sensing is vulnerable to the spectrum sensing data falsification (SSDF) attack. Specifically, a malicious user can send a falsified sensing report to mislead other (benign) SUs to make an incorrect decision on the PU activity, to cause either denial of service to benign SUs or harmful interference to PUs. Therefore, detecting the SSDF attack is extremely important for robust cooperative spectrum sensing. This paper proposes a distributed defense scheme, termed conjugate prior based SSDF detection (CoPD), to countermeasure the SSDF attack. CoPD can effectively exclude the malicious sensing reports from SSDF attackers, so that benign SUs can effectively detect the PU activity. Furthermore, CoPD can also exclude abnormal sensing reports from ill-functioned SUs. Simulation results indicate that CoPD achieves very good performance to accomplish robust cooperative spectrum sensing.     

Neighbor Detection-Based Spectrum Sensing Algorithm in Distributed Cognitive Radio Networks

Security issues of spectrum sensing have drawn a lot of attentions in Cognitive radio networks (CRNs). Malicious users can m islead the network to m ake wrong decision about the states of channels by tampering spectrum sensing data. To defense against Spectrum sens-ing data falsification (SSDF) attack, we propose a neighbor detection-based spectrum sensing algorithm in distributed CRNs, which can detect attackers with the help of neigh-bors during spectrum sensing to improve the accuracy of decision making. The proposed scheme can also guarantee the connectivity of the network. Simulation results illus-trate that the proposed scheme can defense against SSDF attacks effectively and reach the unified information of spectrum sensing data.     

Sensor node-assisted asynchronous cooperative spectrum sensing for cognitive radio network

In order to take advantage of the asynchronous sensing information, alleviate the sensing overhead of secondary users （SUs） and improve the detection performance, a sensor node-assisted asynchronous cooperative spectrum sensing （SN-ACSS） scheme for cognitive radio （CR） network （CRN） was proposed. In SN-ACSS, each SU is surrounded by sensor nodes （SNs）, which asynchronously make hard decisions and soft decisions based on the Bayesian fusion rule instead of the SU. The SU combines these soft decisions and makes the local soft decision. Finally, the fusion center （FC） fuses the local soft decisions transmitted from SUs with different weight coefficients to attain the final soft decision. Besides, the impact of the statistics of licensed band occupancy on detection performance and the fact that different SNs have different sensing contributions are also considered in SN-ACSS scheme. Numerical results show that compared with the conventional synchronous cooperative spectrum sensing （SCSS） and the existing ACSS schemes, SN-ACSS algorithm achieves a better detection performance and lower cost with the same number of SNs.     

Mitigating spectrum sensing data falsification attack in ad hoc cognitive radio networks

In a cognitive radio ad hoc network, there is no central authority. Hence, distributed collaborative spectrum sensing (CSS) plays a major role in achieving an accurate spectrum sensing result. However, CSS is sensitive to spectrum sensing data falsification (SSDF) attack, in which a malicious user falsifies its local sensing report before disseminating it into the network. To capture such abnormal behavior of a node, we present an approach for detecting SSDF attack based on dissimilarity score. A secondary user (SU) computes the dissimilarity score of its neighbors from the messages received from its h‐hop neighbors. Further, we also present how the proposed scheme can be used on the sequence of sensing reports to detect and isolate the malicious SUs on the fly.