基于ESD测试的无线电网络抗SSDF攻击协作频谱感知方案

针对认知无线电网络中协作频谱感知容易遭受数据伪造攻击的问题,提出一种基于检验统计和极端学生化偏差检验法的协作频谱感知方案。首先,将差分进化算法与加权增益合并软决策融合方法相结合,形成一种高效的节点决策融合机制。然后,在协作感知中,根据节点的软决策数据,利用检验统计消除故障认知节点。最后,利用提出的改进型ESD检验法消除恶意认知节点,从而形成全局决策。仿真结果表明,该方案在协作感知中能够有效过滤SSDF攻击数据,具有较低的误检测率。     

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。      

Towards trustworthy collaboration in spectrum sensing for ad hoc cognitive radio networks

Cognitive radio networks (CRN) make use of dynamic spectrum access to communicate opportunistically in frequency bands otherwise licensed to incumbent primary users such as TV broadcast. To prevent interference to primary users it is vital for secondary users in CRNs to conduct accurate spectrum sensing, which is especially challenging when the transmission range of primary users is shorter compared to the size of the CRN. This task becomes even more challenging in the presence of malicious secondary users that launch spectrum sensing data falsification (SSDF) attacks by providing false spectrum reports. Existing solutions to detect such malicious behaviors cannot be utilized in scenarios where the transmission range of primary users is limited within a small sub-region of the CRN. In this paper, we present a framework for trustworthy collaboration in spectrum sensing for ad hoc CRNs. This framework incorporates a semi-supervised spatio-spectral anomaly/outlier detection system and a reputation system, both designed to detect byzantine attacks in the form of SSDF from malicious nodes within the CRN. The framework guarantees protection of incumbent primary users’ communication rights while at the same time making optimal use of the spectrum when it is not used by primary users. Simulation carried out under typical network conditions and attack scenarios shows that our proposed framework can achieve spectrum decision accuracy up to 99.3 % and detect malicious nodes up to 98 % of the time.     

Throughput of cognitive radio networks with improved energy detector under security threats

Spectrum sensing in cognitive radio networks (CRNs) is subjected to some security threats such as primary user emulation (PUE) attack and spectrum sensing data falsification (SSDF) attack. In PUE attack, a malicious user (MU_PUE) transmits an emulated primary signal throughout the spectrum sensing interval to secondary users (SUs) to forestall them from accessing the primary user (PU) spectrum bands. In SSDF attack, malicious users (MU_SSDF) intentionally report false sensing decisions to the fusion center (FC) to influence the overall decision. While most of the existing literatures have studied the effects of these 2 types of attacks separately, the present paper evaluates the secondary network performance in terms of throughput under both the PUE and SSDF attacks with improved energy detectors (IEDs) where SU's spectrum access is hybrid, ie, either in overlay or in underlay mode. An analytical expression on throughput of SU under the simultaneous influence of both of these attacks is developed. Impact of several parameters such as IED parameter, attacker probabilities, and attacker strength on the throughput of SU is investigated. Performance of the present scheme is also compared with only PUE and only SSDF attacks. A simulation test bed is developed in MATLAB to validate our analytical results.     

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...     

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.     

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...     

分布式协作认知无线电SSDF攻击的防御策略综述

协作频谱感知可以提高频谱感知的可靠性,但其融合机制使得其易受频谱感知数据篡改(spectrum sensing data falsification,SSDF)攻击影响.为深入了解SSDF攻击防御算法的发展现状及趋势,总结了SSDF攻击典型的分布式防御策略,并对相应分布式防御算法进行了比较.最后依据目前相关研究中所发现的问题,对SSDF攻击分布式防御策略未来的研究方向进行了展望.     

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.     

An artificial intelligence-based spectrum sensing methodology for LoRa and cognitive radio networks

The artificial intelligence-based spectrum sensing approach is extremely important in terms of effective bandwidth utilization for low power wide area networks (LPWANs) based on cognitive radio networks (CRNs). Most studies perform spectrum detection with CRNs using optimization or deep neural network methods. However, optimization-based spectrum detection approaches based on current LPWANs are scarce. For this purpose, in this study, a hybrid optimization methodology integrated with CRNs is proposed for LoRa, which is one of the most compatible LPWAN technologies in the Internet of Things (IoTs) recently. In the particle swarm optimization (PSO) part of this hybrid methodology, agent users are created so that secondary users (SUs) could use the licensed band of primary users (PUs) in cognitive radio. On the genetic algorithm side, LoRa error rates are minimized in order to further improve the performance of the proposed method. In this way, effective spectrum sensing is performed in the LoRa network. Various LoRa-CRN experiments have been carried out in the simulation environment, and the probability of detection and false alarm performances have been compared with both theoretical and proposed approaches in terms of quality estimation parameters. It is clear from the results that the proposed methods give successful results for the LoRa-CRNs.     

可防御SSDF攻击的宽带压缩频谱感知

SSDF（Spectrum Sensing Data Falsification）攻击是认知无线网络中对频谱感知性能危害最大的攻击方式之一。基于认知无线网络中信号频域的固有稀疏性,本文结合了压缩感知(CS)技术与平均一致(average consensus)算法,建立了可防御SSDF攻击的分布式宽带压缩频谱感知模型。本文建立了次用户的声望值指标,用以在分布式信息融合的过程中更加准确地排除潜在的恶意次用户影响。在感知阶段,各个CR节点对接收到的主用户信号进行压缩采样以减少对宽带信号采样的开销和复杂度,并做出本地频谱估计。在信息融合阶段,各CR节点的本地频谱估计结果以分布式的方式进行信息融合,排除潜在恶意次用户的影响,得到最终的频谱估计结果。仿真结果表明,本文提出的分布式频谱感知模型可以有效地抵御SSDF攻击,提高了频谱感知的性能。      

Cyclostationarity-Based Decision Reporting Scheme for Cooperative Spectrum Sensing

Cooperative spectrum sensing has emerged as a promising solution to the hidden terminal problem in cognitive radio networks (CRNs). It could significantly promote the sensing capability of CRNs by exploiting space diversity gains in a fading environment. However, owing to the decision reporting overhead, there exists a tradeoff between the system throughput and performance of cooperative spectrum sensing. In this paper, we propose a cyclostationarity-based decision reporting scheme for cooperative spectrum sensing in CRNs with cyclic delay diversity orthogonal frequency division multiplexing (CDD-OFDM). Decision information would be embedded into the CDD-OFDM signals in terms of cyclostationary signatures and shared among cognitive radio (CR) users along with data transmissions. As a result, satisfied system throughput could be achieved without additional spectral or temporal resources consumption when the number of cooperative users increases. Numerical results are presented to show the system throughput enhancement.     

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。     

A preload cooperative sensing scheme with low overhead in cognitive radio networks

In cognitive radio networks (CRNs), users can collaborate to improve the accuracy of spectrum sensing, but a large number of secondary users reporting their local sensing results may create significant overhead. In this paper, we propose a new pre‐sensing scheme, called preload cooperative sensing (PCS), which not only attains the given sensing accuracy for CRNs but also reduces the whole sensing time T. In order to reduce the sensing overhead in CRNs, the proposed scheme adopts two key technologies: selective reporting technology and pre‐sensing sequential detection technology. Selective reporting technology implies that only those users, which detect the presence of primary users, need to report the results, while pre‐sensing sequential detection technology is an asynchronous parallel scheme, which sets a threshold to determine the presence of primary users. Considering the preload sensing slots, we derive a formula to express the overall miss detection probability, and at a given Quality of Service (QoS) value, the sensing overheads of PCS are analyzed over Rayleigh fading channel. Also, we consider the overhead minimization problems in PCS. Simulation results show the superiority and efficiency of the PCS scheme. Copyright © 2015 John Wiley & Sons, Ltd.     

基于非参量累积和的合作频谱感知

针对认知无线电网络中频谱感知的检测时延降低问题,提出了基于非参量累积和的合作频谱感知算法。本地认知用户预处理频谱观测数据,获得观测数据相对于信念值的正向漂移和负向漂移。为了缩短检测延迟,认知用户只将数据的正向漂移同步传输至融合中心。融合中心融合正向漂移得到判决信息,采用非参量累积和算法依时间序列顺序累加判决信息,判断主用户是否正在使用授权频段。为了解决不传输负向漂移引起的虚警问题,改进算法提出融合中心可以保留首次判决,经过等待时间间隔后再作出最终判决。相对于传统的软融合算法,改进融合规则的合作频谱感知算法具有较低的检测延迟。     

模拟人群信任和决策机制的协作频谱感知方法

通过模拟人群内部的信任和决策机制,针对多用户的频谱协作感知一致性问题,提出了一种分布式算法。该算法首先通过网络的历次协作过程预测出各感知用户的动态可信值,据此产生用户的相对可信值,并结合决策机制使得用户之间进行数据交互,随着数据的可信、迭代交互,所有用户状态将趋于一致,最后通过判定算法得出最终结果。算法充分考虑了实际环境中各用户频带感知能力的不平衡性,而且各次级用户只需要进行少量局部数据交换即可实现协作感知,与传统的OR-rule、1-out-of-N rule以及普通迭代法有较大区别。对3种数据篡改攻击进行了分析,并在预测算法的基础上提出了相应的安全策略。理论分析与仿真结果表明,新算法在准确性和安全性上均优于传统合作频谱感知算法,能显著提高频谱感知准确率,同时兼具较强的防攻击能力。     

Binary decision-based collaborative spectrum sensing in infrastructure-less cognitive radio network

Collaborative spectrum sensing (CSS) plays a vital role in achieving accurate spectrum sensing results in a cognitive radio network (CRN). However, CSS in infrastructure-less CRNs is a challenging task as there is no central authority which aggregates individual sensing reports to take a final decision. Hence, it becomes the responsibility of each node to aggregate sensing reports of other nodes to take the final decision. Towards this end, we present two collaborative spectrum sensing techniques for infrastructure-less CRN based on message passing. The first technique is distributed binary decision-based CSS (DBCSS), in which a node broadcasts its sensing report and uses the binary decisions of its h-hop neighbors for making its final sensing decision. The second one is distributed cluster-based CSS (DCCSS) in which a cluster head consolidates the sensing reports in its cluster into a message and broadcast this message in the network. We support the validity of the proposed schemes through extensive simulation results.     

Weight sequential log-likelihood ratio detect algorithm with malicious users removing

Due to the openness of the cognitive radio network, spectrum sensing data falsification (SSDF) can attack the spectrum sensing easily, while there is no effective algorithm proposed in current research work, so this paper introduces the malicious users removing to the weight sequential probability radio test (WSPRT). The terminals' weight is weighted by the accuracy of their spectrum sensing information, which can also be used to detect the malicious user. If one terminal owns a low weight, it can be treated as malicious user, and should be removed from the aggregation center. Simulation results show that the improved WSPRT can achieve higher performance compared with the other two conventional sequential detection methods under different number of malicious users.     

A collaborative spectrum sensing scheme using fuzzy comprehensive evaluation in cognitive radio

Cognitive Radio (CR) is a promising technology to solve the challenging spectrum scarcity problem.However,to implement CR,spectrum sensing is the groundwork and the precondition.In this paper,a collaborative spectrum sensing scheme using fuzzy comprehensive evaluation is proposed.The final sensing decision of the proposed scheme is based on the combination of distributed sensing results of different Secondary Users (SUs).To improve the reliability of the sensing decision,the combination procedure takes into account the credibility of each SU,which is evaluated using fuzzy comprehensive evaluation.The effect of the presence of malicious SUs and malfunctioning SUs on the performance of the proposed scheme is also investigated.The efficiency of the scheme is validated through analysis and simulation.     

基于随机矩阵理论的非重构宽带压缩频谱感知方法

该文采用随机矩阵理论(RMT)直接对压缩采样得到的观测数据进行分析,设计出了一种基于广义似然比检验(GLRT)的非重构宽带压缩频谱感知新算法。该算法无需任何先验知识就能对宽带频谱中的每个子带进行盲检测。此外,为了减轻次用户(SU)在数据获取和频谱感知过程中的通信开销,该文提出一种基于传感器节点(SN)辅助感知的合作频谱感知架构。理论分析和仿真结果均表明,与传统基于信号重构的GLRT感知算法以及Roy最大根检测(RLRT)算法相比,该算法不仅具有计算复杂度低、开销小、感知性能稳定等诸多优点;而且只需较少的SN就能获得较好的检测性能。