Cooperative wideband sensing based on cyclostationary features with multiple malicious user elimination

Spectrum sensing is an essential concept in cognitive radio. To overcome the single node sensing issue that arises due to channel impediments, cooperative/multinode sensing is being used. Although cooperation among multiple cognitive users enhances the sensing performance, presence of few malicious cognitive users may severely degrade the efficiency of the system. In this paper, generalized extreme studentized deviate (GESD) and adjusted box-plot (ABP) methods are introduced to increase the sensing reliability of cooperative network by eliminating multiple malicious cognitive users. The performance of the cyclostationary feature detection method is compared with the energy detection method under different channel impediments. The simulation results are carried out with false alarm probability of 0.01 and a detection probability of 0.9. The simulation results reveal that there is a significant improvement in cooperative sensing performance by elimination of multiple malicious user in the network.     

Multiregional secure localization using compressive sensing in wireless sensor networks

Security and accuracy are two issues in the localization of wireless sensor networks (WSNs) that are difficult to balance in hostile indoor environments. Massive numbers of malicious positioning requests may cause the functional failure of an entire WSN. To eliminate the misjudgments caused by malicious nodes, we propose a compressive‐sensing–based multiregional secure localization (CSMR_SL) algorithm to reduce the impact of malicious users on secure positioning by considering the resource‐constrained nature of WSNs. In CSMR_SL, a multiregion offline mechanism is introduced to identify malicious nodes and a preprocessing procedure is adopted to weight and balance the contributions of anchor nodes. Simulation results show that CSMR_SL may significantly improve robustness against attacks and reduce the influence of indoor environments while maintaining sufficient accuracy levels.     

考虑恶意节点的CRN合作频谱感知方法

基于带有恶意节点的更为实际的频谱感知环境,研究了基于合作感知的频谱共享网络模型,次级用户将会根据合作感知结果动态地调整其发射功率。为了防止恶意节点对感知系统的感知性能造成严重影响,研究了如何进行合作感知以提高感知性能。在一定的检测概率和相关功率约束下,建立了一个以最大化次级网络的吞吐量为目标函数的优化问题。仿真实验首先突出说明了恶意节点数目对频谱感知影响重大,同时还表明无论是否存在恶意节点,提出的算法均可有效地计算出最优的感知时间和发射功率,且在降低最大干扰功率限制和最大发射功率限制时,网络的吞吐量是增大的。     

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.     

一种SSDF攻击检测的证据理论合作频谱感知算法

现有的基于证据理论的合作频谱感知认为所有认知用户都是诚实的,没有考虑恶意用户的存在。当恶意用户篡改本地感知的结果,发送错误的数据到数据融合中心,将会降低合作频谱感知的性能,这种攻击称为频谱感知数据篡改（spectrum sensing data falsification,SSDF）攻击。由于恶意用户发送的证据与其它认知用户的证据存在差别,本文使用Jousselme距离来衡量证据的可信度,提出一种SSDF攻击检测算法。数据融合中心接收所有认知用户的证据,让可信度低的证据不参与融合判决,可信度高的证据根据可信度进行加权融合。仿真结果表明,所提出的SSDF攻击检测算法在恶意用户发起SSDF攻击时可以很好的改善频谱感知性能。      

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。     

基于随机共振的双门限协作频谱感知算法

针对现有频谱感知算法在低信噪比（SNR）环境中性能检测不佳的问题以及传统随机共振（SR）检测弱信号的方法在实际应用中存在的局限性,通过设置最优门限,计算出最优的协作用户数量,提出了一种基于随机共振的双门限协作频谱感知算法,并对提出的算法进行了性能分析。DCSSR算法通过将位于双门限不确定区域的统计数据经过随机共振系统,进一步提高频谱感知算法在低信噪比下的检测性能。仿真结果表明,在不同信噪比和虚警概率下,DCSSR算法相较于传统单门限能量协作算法、双门限能量协作算法以及单门限随机共振协作算法,检测性能都得到了提升。在信噪比为-20 dB时,提出的DCSSR算法相较于传统单门限能量检测协作算法,检测概率提高了80%。     

基于信任节点辅助的安全协同频谱感知策略

在认知无线电网络中,多个用户相互协作进行频谱感知能有效地提高系统感知性能。然而这种协同方式也带来了新的安全隐患:当恶意用户出现时,现有协同感知方法无法确保感知结果的鲁棒性。本文针对这一问题,提出了一种基于信任节点辅助的安全协同感知策略。该策略通过借助网络中信任节点的感知结果,在用户域和时间域两个维度上消除恶意用户的影响,确保了算法在较多恶意用户环境中的稳定性。仿真结果表明,新算法的性能优于Kaligineedi所提算法,在恶意用户数目为网络用户总数一半时,仍能有效地进行协同感知,具有良好的鲁棒性。      

基于可信度的半双工协同频谱检测

提出了基于可信度的半双工协同频谱检测方案.该方案利用贝叶斯方法来确定协同用户的可信度,分别就具有单协同用户和多协同用户的半双工协同频谱检测进行了研究,分析了可信度对协同频谱检测的影响.分析和仿真结果表明,本文策略不但能提高检测概率、降低检测时间,还能排除故障用户对检测性能的影响、减少恶意用户的干扰.     

Distributed cooperative spectrum sensing based on reinforcement learning in cognitive radio networks

Spectrum sensing is an initial task for the successful operation of cognitive radio networks (CRN). During cooperative spectrum sensing, malicious secondary user (SU) may report false sensing data which would degrade the final aggregated sensing outcome. In this paper, we propose a distributed cooperative spectrum sensing (CSS) method based on reinforcement learning (RL) to remove data fusion between users with different reputations in CRN. This method regards each SU as an agent, which is selected from the adjacent nodes of CRN participating in CSS. The reputation value is used as reward to ensure that the agent tends to merge with high reputation nodes. The conformance fusion is adopted to promote consensus of the whole network, while it’s also compared with the decision threshold to complete CSS. Simulation results show that the proposed method can identify malicious users effectively. As a result, the whole CRN based on RL is more intelligent and stable.     

最大系统效用合作频谱感知优化算法

认知系统性能可以通过合作频谱感知技术的应用得到显著提高,但是由于随着参与频谱感知的认知用户数量增加,而必然会导致系统资源消耗增大。在综合评估系统性能和系统资源效率效用的基础上,通过科学选取感知时长和认知用户数量的策略,提出最大系统效用合作频谱感知优化算法。仿真结果表明,在系统探测概率确定的条件下,通过选择恰当的认知用户数量和感知时长,认知系统效用可以提升到最高。      

基于比特承诺的可撤销匿名电子检举系统

一个安全的电子检举系统必须满足检举人身份匿名性的要求,保证检举人的隐私,同时在必要的情况下,检举系统也能够提供匿名的撤销。Qiu等提出了一种基于群签名的可撤销匿名的电子检举系统,提供了可撤销的匿名检举,并满足一序列安全性要求,但基于群签名的方案对于大量群成员存在着无法避免的效率问题。文中基于比特承诺协议,提出了一种可撤销匿名的检举方案。该方案在提供了举报过程中对于举报者身份的匿名性的同时,对于可能的恶意举报,在电子检举中心（EIC）和可信第三方（TTP）的协作下,能够撤销匿名,追查责任人。该方案可适用于不同规模的使用人群。     

基于 DS 证据理论的协作频谱感知改进方法

提出了一种新的协作频谱感知方法,该方法采用DS证据理论。首先,在本地感知通过投影近似法分解感知结果以减少次级用户上报至融合中心的感知数据,从而降低协作带宽开销。其次,在融合中心根据次级用户证据矢量之间的距离计算可信度,以可信度为权重对每个次级用户的基本概率分配进行加权平均来减小高度冲突数据对检测性能的影响。仿真结果表明,该方法减少了协作感知的带宽开销,改善了数据高度冲突时的检测性能。     

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.     

一种新的分布式协作能量检测算法

针对集中式协作能量检测算法严重依赖中心节点的问题,提出了一种基于一致滤波器的完全分布式的协作能量检测算法.该算法不需要中心节点,次用户可以通过与其邻居次用户进行局域的数据交换,实现基于数据级融合的协作频谱感知.仿真结果表明,该算法比现有的集中式协作能量检测算法优越,能够实现安全的频谱感知.     

Reinforcement learning for cooperative sensing gain in cognitive radio ad hoc networks

Spectrum sensing is a fundamental function in cognitive radio networks for detecting the presence of primary users in licensed bands. The detection performance may be considerably compromised due to multipath fading and shadowing. To resolve this issue, cooperative sensing is an effective approach to combat channel impairments by cooperation of secondary users. This approach, however, incurs overhead such as delay for reporting local decisions and the increase of control traffic. In this paper, a reinforcement learning-based cooperative sensing (RLCS) method is proposed to address the cooperation overhead problem and improve cooperative gain in cognitive radio ad hoc networks. The proposed algorithm is proven to converge and capable of (1) finding the optimal set of cooperating neighbors with minimum control traffic, (2) minimizings the overall cooperative sensing delay, (3) selecting independent users for cooperation under correlated shadowing, and (4) excluding unreliable users and data from cooperation. Simulation results show that the RLCS method reduces the overhead of cooperative sensing while effectively improving the detection performance to combat correlated shadowing. Moreover, it adapts to environmental change and maintains comparable performance under the impact of primary user activity, user movement, user reliability, and control channel fading.     

Fault-Tolerant Cooperative Spectrum Sensing Scheme for Cognitive Radio Networks

In order to solve the uncertainty resulting from shadowing effect and resist the attack from malicious cognitive radio (CR) users, we propose a fault-tolerant cooperative spectrum sensing scheme for CR networks, where an energy detection-based local spectrum sensing is performed at each CR user, a coefficient is used to weight each CR user’s sensing result, a linear weighted fusion process is performed at the fusion center (FC) to combine received sensing results. For a fault-tolerant cooperative spectrum sensing scheme, the most important issue is to distinguish whether the CR user is reliable or not. In this paper, a reputation-based cooperative mechanism is presented to alleviate the influence of the unreliable sensing results from CR users suffering shadowing and the false sensing data from malicious CR users on the detection result at the FC. In proposed fault-tolerant cooperative scheme, each cooperative CR user has a reputation degree which is initialized and adjusted by the FC and used to weight the sensing result from the corresponding user in the fusion process at the FC. And then, two reputation degree adjusting methods are presented to manage the reputation degree of each CR user. Simulation results show that the proposed scheme can not only weaken the harmful influence caused by malicious CR users, but also alleviate the corrupted detection problem resulting from destructive channel condition between the primary transmitter and the CR user. Moreover, the detection performance of the fault-tolerant cooperative scheme, which has a feasible computational complexity and needs no instantaneous SNRs, is close to that of the optimal scheme.     

Fast and Accurate Detection of Malicious Users in Cooperative Spectrum Sensing Network

Cooperative spectrum sensing allows users to detect available spectrum and utilize it. However, it is known that a few users can easily affect the cooperative decision at the fusion center by reporting falsified sensing data. The problem of detecting malicious users in cooperative spectrum sensing has been studied by numerous researchers. The basic approach is to compute the credibility of the reported data of each user and declare those with low credibility (below some threshold) as malicious. The computation of credibility can be based on several things such as pattern of historic behavior, entropy of the reported data, signal-to-noise ratio, etc. The credibility of the reported data of some user node can be expressed as an attack probability of that node. A node with higher attack probability will have lower credibility while a node with lower attack probability a higher credibility. Then, the problem of computing credibility of a node becomes the problem of computing attack probability of a node given a set of reported data. We can enumerate a list of all possible attack probability vectors for a set of nodes and the list of all possible energy level vectors of the channel for the observed time period, and compute which combination of attack probability and channel energy level vector can have the maximum probability to produce the reported channel energy level. However, the search space is quite large and grows exponentially as the number of user nodes and the number of time slots to observe increase. In this paper, we suggest algorithms that reduce the search space considerably and detect malicious users in linear time instead of exponential time. The suggested algorithms have been implemented and show promising results.

     

认知无线电网络中基于跨层设计的能效优化算法

为提高认知无线电网络中次用户节点的能量有效性,该文基于连续时间马尔科夫理论对次用户的频谱感知和接入过程进行联合建模,对影响次用户传输能效的主要因素进行了分析,提出了一种基于跨层设计的能量有效优化算法。该算法可有效减小主用户非时隙返回信道对次用户能量有效性的影响,并通过联合优化感知时间和接入概率,使次用户在感知性能和传输能效间实现了有效折衷。仿真结果表明,本文算法相对于仅考虑频谱感知或接入策略的单层优化算法,可使次用户的能量有效性得到较大提高。      

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。