Spectrum Sensing Data Falsification Research Articles

Bayesian-Inference-Based Sliding Window Trust Model Against Probabilistic SSDF Attack in Cognitive Radio Networks

Collaborative spectrum sensing as a promising approach is widely investigated for identifying available spectrum in cognitive radio networks. However, such cooperation is vulnerable to be threatened by spectrum sensing data falsification (SSDF) attacks. Many previous attacker detection schemes rely on some strong assumptions, such as prior knowledge of the local performance of each secondary user (SU) and higher attack probability. To overcome these drawbacks, in this article, we develop a Bayesian-inference-based sliding window trust model to identify and weed out independent and collaborative probabilistic SSDF attackers without any prior knowledge of the attack behaviors. Based on this model, the trust value of each SU is obtained by combining the weighted intermediate sensing information from multiple small sliding windows. A sigmoid-log-function-based scheme is introduced to mitigate trust value fluctuations caused by different attacking probabilities. Finally, extensive simulation results show that compared with existing schemes, the proposed approach can identify probabilistic SSDF attackers effectively and isolate them with a higher detection rate across a wide range of attacking scenarios.

On MMD-Based Secure Fusion Strategy for Robust Cooperative Spectrum Sensing

Collaborative spectrum sensing (CSS) in cognitive radio-based networks (CRNs) is vulnerable to spectrum sensing data falsification (SSDF) attack. Existing defense mechanisms are commonly subject to major limitations with unrealistic assumptions which can be easily violated in future wireless networks. Such assumptions include the number of honest users is in majority, the attackers’ flip rates are identical and fixed, the adoption of hard decision approach in the fusion strategy and only TV sets are considered as the primary users to be protected. Essentially, all existing representative schemes utilize certain low-dimensional human-observed metric to distinguish malicious users and honest users based on domain knowledge. Therefore, these defense mechanisms cannot perform properly under certain condition, such as sensing reports with different distributions but have equal mean and variance. In this paper, we propose a secure fusion strategy which adopts “soft decision” method and can distinguish malicious users and honest users under any distribution of sensing reports using maximum mean discrepancy (MMD). Our proposed CSS scheme is suitable for general CRN application scenarios. The simulation results reflect the effects of different kernel functions and window size on the system performance, and show our proposed defense mechanism outperforms the existing works.

Mitigation strategy against spectrum-sensing data falsification attack in cognitive radio sensor networks

To detect the primary user’s activity accurately in cognitive radio sensor networks, cooperative spectrum sensing is recommended to improve the sensing performance and the reliability of spectrum-sensing process. However, spectrum-sensing data falsification attack being launched by malicious users may lead to fatal mistake of global decision about spectrum availability at the fusion center. It is a tough task to mitigate the negative effect of spectrum-sensing data falsification attack and even eliminate these attackers from the network. In this article, we first discuss the randomly false attack model and analyze the effects of two classes of attacks, individual and collaborative, on the global sensing performance at the fusion center. Afterwards, a linear weighted combination scheme is designed to eliminate the effects of the attacks on the final sensing decision. By evaluating the received sensing result, each user can be assigned a weight related to impact factors, which includes result consistency degree and data deviation degree. Furthermore, an adaptive reputation evaluation mechanism is introduced to discriminate malicious and honest sensor node. The evaluation is conducted through simulations, and the results reveal the benefits of the proposed in aspect of mitigation of spectrum-sensing data falsification attack.

PROLEMus: A Proactive Learning-Based MAC Protocol Against PUEA and SSDF Attacks in Energy Constrained Cognitive Radio Networks

Malicious users can exploit vulnerabilities in cognitive radio networks (CRNs) and cause heavy performance degradation by denial of service (DoS) attacks. During operation, cognitive radios (CRs) spend a considerable amount of time to identify idle (free) channels for transmission. In addition, CRs also need additional security mechanisms to prevent malicious attacks. Proactive model predictive control (MPC)-based medium access control (MAC) protocols for CRs can quicken the idle channel identification by predicting future states of channels in advance. This provides enough time for CRs to carry out other calculations like DoS attack detection. However, such external detection techniques use additional power that makes them inappropriate for energy constrained applications. As a solution, this paper proposes a proactive learning-based MAC protocol (PROLEMus) that shows immunity to two prominent CR-based DoS attacks, namely, primary user emulation attack (PUEA) and spectrum sensing data falsification (SSDF) attack, without any external detection mechanism. PROLEMus shows an average of 6.2%, 8.9%, and 12.4% improvement in channel utilization, backoff rate, and sensing delay, respectively, with low prediction errors (≤1.8%) saving 19.65% energy, when compared to recently proposed MAC protocols like ProMAC aided with additional DoS attack detection mechanism.

Secure differential compressive spectrum sensing with 1‐bit quantisation

In this study, the authors propose a secure cooperative spectrum sensing algorithm that is based on compressive sampling theory and employed in wide-band cognitive radio network. The proposed algorithm seeks to reduce the hardware complexity of the spectrum sensing node by reducing the reported data from this node to the fusion centre and/or other sensing nodes. Due to the spatial correlation between the received primary users' signals at the sensing nodes, the difference between the measurement vectors of any two sensing nodes is expected to be very small when it is compared with the value of the measurement vector itself. The expected reduction in the dynamic range of the difference measurement vector motivates to use the 1-bit quantiser where only 10bit per measurement is retained, representing its sign. Moreover, the proposed algorithm is impregnable against spectrum sensing data falsification attacks. Both the fusion centre and sensing nodes utilise the proposed authentication algorithm to pick out data from the trustful nodes only to estimate its output data. According to the simulation results, the proposed system guarantees more safeguard to the license primary user while preserves the unlicensed secondary user throughput.

A Double Adaptive Approach to Tackle Malicious Users in Cognitive Radio Networks

Cognitive radio (CR) is being considered as a vital technology to provide solution to spectrum scarcity in next generation network, by efficiently utilizing the vacant spectrum of the licensed users. Cooperative spectrum sensing in cognitive radio network has a promising performance compared to the individual sensing. However, the existence of the malicious users’ attack highly degrades the performance of the cognitive radio networks by sending falsified data also known as spectrum sensing data falsification (SSDF) to the fusion center. In this paper, we propose a double adaptive thresholding technique in order to differentiate legitimate users from doubtful and malicious users. Prior to the double adaptive approach, the maximal ratio combining (MRC) scheme is utilized to assign weight to each user such that the legitimate users experience higher weights than the malicious users. Double adaptive threshold is applied to give a fair chance to the doubtful users to ensure their credibility. A doubtful user that fails the double adaptive threshold test is declared as a malicious user. The results of the legitimate users are combined at the fusion center by utilizing Dempster-Shafer (DS) evidence theory. Effectiveness of the proposed scheme is proved through simulations by comparing with the existing schemes.

A low complexity cluster based data fusion to defense against SSDF attack in cognitive radio networks

In cognitive radio networks, the security attacks disrupt the spectrum sensing process. One of the most threatening attacks is spectrum sensing data falsification (SSDF) attack, where some of the network users are malicious users and by sending their incorrect observations degrade the decision making process. In this paper, to counter this kind of attack, we propose a method based on clustering the secondary users and estimating some unknown parameters of their received power. In the proposed scheme, we consider two different scenarios for the attack strategies: deterministic attack and WGN (White Gaussian Noise) attack. Due to the use of clustering and the low computational complexity of the proposed method, the complexity order reduces significantly. Moreover, our scheme has good performance in detecting the malicious users of each cluster.

Securing Cooperative Spectrum Sensing against DC-SSDF Attack Using Trust Fluctuation Clustering Analysis in Cognitive Radio Networks

Cooperative spectrum sensing (CSS) has been recognized as a forceful approach to promote the utilization of spectrum bands. Nevertheless, all secondary users (SU) are assumed as honest in CSS, thus giving opportunities for attackers to launch the spectrum sensing data falsification (SSDF) attack. To defend against such attack, many efforts have been made to trust mechanism. In this paper, we argue that securing CSS with only trust mechanism is not enough and report the description of dynamic-collusive SSDF attack (DC-SSDF attack). To escape the detection of trust mechanism, DC-SSDF attackers can maintain high trust by submitting true sensing data dynamically and then fake sensing data in the collaborative manner to increase their attack strength. Noting that the resonance phenomenon may appear in the trust value curve of DC-SSDF attackers, a defense scheme called TFCA is proposed from the design idea of trust fluctuation clustering analysis to suppress DC-SSDF attack. In the TFCA scheme, the decreasing property of trust value in the resonance phenomenon is adopted to measure the similarity distance between two attackers. Based on the similarity distance computation, the binary clustering algorithm is designed by electing initial binary samples to identify DC-SSDF attackers. Finally, trust mechanism can be perfected by TFCA to correct DC-SSDF attackers’ trust value. Simulation results show that our TFCA scheme can improve the accuracy of trust value calculation, thus reducing the strength of DC-SSDF attack successfully.

AADS: DEVELOPING AGENT ASSISTED DEFENSE SYSTEM AGAINST HARMFUL ATTACKS IN COGNITIVE RADIO NETWORK

Cognitive radio network (CRN) is an emerging technology that provides ability of adapting operating parameters to wireless devices in order to overcome spectrum scarcity problems. The paper mainly focused on three major attacks held on CRN such as, primary user emulation (PUE), spectrum sensing data falsification (SSDF), and eavesdropper attacker. To overcome attacks, this paper designs an effective agent assisted defence system in CRN. Initially network is partitioned into four different groups based on communication range of PBS. To mitigate SSDF attack the malicious node is isolated from the network. This decision is made by tri-state Markov chain model (MCM) which predicts future activity of the node. Communication in the network is protected by hybrid advance encryption with Diffie-Hellman encryption (HAES-DHE) algorithm to alleviate eavesdropper attack. Experimental result shows promising improvements in probability of detection, probability of miss detection, estimation of attack strength, average estimate of honest nodes and throughput.

Malicious user detection with local outlier factor during spectrum sensing in cognitive radio network

In collaborative sensing, multiple secondary users (SUs) cooperate for a more accurate sensing decision to detect spectrum holes in cognitive radio networks (CRNs). This technique, however, can be adversely affected by malicious users (MUs) who route falsified spectrum sensing data to the fusion centre (FC). This attack is known as the spectrum sensing data falsification (SSDF) attack. The task of the FC is to aggregate local sensing reports and is thereby responsible for making the final sensing decision. In this paper, we propose a detection and isolation scheme based on local outlier factor (LOF) to detect and reduce the unfavourable effects of SSDF attack. The key feature of this scheme is that for each SU a metric is calculated, which is called the LOF. Based on the LOF, a decision is made about whether an SU is an attacker or not. We support the validity of the proposed scheme through extensive simulation results.

Employing Game Theory and TDMA Protocol to Enhance Security and Manage Power Consumption in WSNs-Based Cognitive Radio

The rapid development of wireless sensor networks (WSNs) is a significant incentive to contribute to vulnerable applications such as cognitive radio (CR). This paper proposes a Stackelberg game approach to enhance the WSN-based CR security against the spectrum sensing data falsification (SSDF) attack and conserve the consequently lost power consumption. The attack aims to corrupt the spectrum decision by imposing interference power to the delivered reports from the sensor nodes (SNs) to the fusion center (FC) to make a protection level below a specific threshold. The proposed model utilizes the intelligent Stackelberg game features along with the matched filter (MF) to maximize the number of protected reports sent by the SNs to the FC leading to an accurate decision of the spectrum status. Furthermore, the TDMA protocol is utilized to resolve the complexity of employing MF for the spectrum detection to avoid the collision between the delivered reports. The proposed model aims to enhance the number of correctly received reports at the FC, and hence manage the lost energy of reports retransmission due to the malicious attack effect. Moreover, the model can conserve the lost power of the failed communication attempts due to the SSDF attack impact. Simulation results indicate the improved performance of the proposed protection model along with the MF over six different environments against the SSDF attack as compared to two defense schemes, namely, random and equal weight defense strategies.

Cooperative Spectrum Sensing for Cognitive Radio-Wireless Sensors Network Based on OR Rule Decision to Enhance Energy Consumption in Greenhouses

Wireless sensors networks (WSNs) combined with cognitive radio have developed and solved the limited space of the frequency spectrum. In this paper, we propose different types of spectrums sensing and their own decisions depend on the probabilities that applied into fusion center, and how these probabilities’ techniques help to enhance the energy consumption of WSNs. In the same way, the importance of designing balanced distribution between the wireless sensors networks and their own sinks. This research also provides an overview of security issues in CR-WSN, especially in Spectrum Sensing Data Falsification (SSDF) attacks that enforces harmful effects on spectrum sensing and spectrum sharing. We adopt OR rule as four types of CRSN sensing protocolin greenhouses application by using Matlab and Netsim simulators. Our results show that the designing balanced wireless sensors and their sinks in greenhouses are very significant to decrease the energy, which is due to the traffic congestion in the sink range area. Furthermore, by applying OR rule has enhanced the energy consumption, and improved the sensors network lifetime compared to cognitive radio network.

Reputation-Based Spectrum Sensing Strategy Selection in Cognitive Radio Ad Hoc Networks.

Spectrum sensing plays an essential role in the detection of unused spectrum whole in cognitive radio networks, including cooperative spectrum sensing (CSS) and independent spectrum sensing. In cognitive radio ad hoc networks (CRAHNs), CSS enhances the sensing performance of cognitive nodes by exploring the spectrum partial homogeneity and fully utilizing the knowledge of neighboring nodes, e.g., sensing results and topological information. However, CSS may also open a door for malicious nodes, i.e., spectrum sensing data falsification (SSDF) attackers, which report fake sensing results to deteriorate the performance of CSS. Generally, the performance of CSS has an inverse relationship with the fraction of SSDF attackers. On the contrary, independent spectrum sensing is robust to SSDF attacks. Therefore, it is desirable to choose a proper sensing strategy between independent sensing and collaborative sensing for CRAHNs coexisting with various fractions of SSDF attackers. In this paper, a novel algorithm called Spectrum Sensing Strategy Selection (4S) is proposed to select better sensing strategies either in a collaborative or in an independent manner. To derive the maximum a posteriori estimation of nodes’ spectrum status, we investigated the graph cut-based CSS method, through which the topological information cost function and the sensing results cost function were constructed. Moreover, the reputation value was applied to evaluate the performance of CSS and independent sensing. The reputation threshold was theoretically analyzed to minimize the probability of choosing the sensing manner with worse performance. Simulations were carried out to verify the viability and the efficiency of the proposed algorithm.

Detecting Byzantine Attack in Cognitive Radio Networks by Exploiting Frequency and Ordering Properties

Collaborative spectrum sensing in cognitive radio network (CRN) helps in increasing the accuracy of detection of the incumbent signal. Each node in an infrastructure-based CRN sends its local sensing report to the fusion center (FC), which aggregates the reports to arrive at a final sensing decision. However, collaborative sensing is vulnerable to the Byzantine attack (also known as spectrum sensing data falsification attack) in which malicious nodes falsify sensing reports in order to adversely affect the final sensing decision. Temporal data collected at the FC in the form of the sensing reports form a rich audit data set, which can be analyzed to identify malicious nodes, if any. In this paper, we present two anomaly detection techniques based on such an analysis. First, we exploit the frequency property of such statistical data and develop a lightweight intrusion detection scheme. Second, we use the Markov chain model which is based on the ordering property of the data to develop another intrusion detection scheme. Simulation results along with theoretical analysis show the validity of the two schemes.

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

SummaryIn 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.

Energy Efficiency in Cooperative Cognitive Radio Network in the Presence of Malicious Users

Energy efficiency (EE) in cooperative cognitive radio networks (CCRNs) enhances opportunistic data transmission through efficient utilization of power. A novel CCRN with a frame structure accommodating cooperative spectrum sensing (CSS) and cooperation in primary user's (PU) transmission or opportunistic secondary users’ (SU) transmission is considered aiming to maximize EE. A set of optimal values for spectrum sensing (SS) time, self, and cooperative transmission power gains are calculated under the constraints of SU power budget, PU detection probability, and SU and PU spectrum efficiency. The proposed system is further investigated in the presence of two separate malicious operations, namely PU emulation operation and spectrum sensing data falsification followed by their detection and weight allocation in data transmission of SU. The goal here is to analyze and evaluate the consequent gain in EE for the CCRN while maintaining the same SS reliability and target PU's transmission rate. Extensive results with a target set of parameters highlight the efficacy of the system.

Securing Cooperative Spectrum Sensing Against Collusive False Feedback Attack in Cognitive Radio Networks

Cooperative spectrum sensing (CSS) is considered as a powerful approach to improve the utilization of scarce spectrum resources. However, CSS assumes that all secondary users (SU) always are honest, thus offering opportunities for attackers to launch spectrum sensing data falsification (SSDF) attack. To combat such threat, recent efforts have been made to trust mechanism. In this paper, we argue that powering CSS with trust mechanism is not enough. A vulnerability for trust mechanism is found, that is, the feedback data from initiator SUs are generally unchecked. Such feedback data corresponding to the spectrum status of primary users can be exploited by attackers to disturb trust mechanism in a collusive manner. Especially, collusive false feedback (CFF) attackers would form a clique to help each other. One of CFF attackers can disguise as an initiator SU who sends the feedback in accordance with the sensing data of their conspirators who play the role of cooperating SUs, resulting in promoting their conspirators' sensing trust quickly. With high sensing trust, attackers can avoid the detection of trust mechanism and, hence, launch SSDF attack (fake sensing data) more easily. We conduct an in-depth investigation on CFF attack and propose a two-level defense scheme called FeedGuard from the design ideas of feedback trust and I-C frequency correlation analysis. Simulation results show that the FeedGuard scheme can enhance the accuracy of sensing trust evaluation, thus successfully reducing the power of CFF attack against CSS.

Attack-Aware Defense Strategy: A Robust Cooperative Spectrum Sensing in Cognitive Radio Sensor Networks

Cognitive radio (CR) is a key enabling technology of dynamic spectrum access to achieve better utilization of frequency spectrum. Cooperative spectrum sensing (CSS) exploits the spatial diversity between CR users to improve the accuracy of spectrum sensing. But, in practice, the reliability of the CSS is vulnerable to spectrum sensing data falsification (SSDF) attack. In this type of attack, some malicious CR sensors intentionally report falsified local sensing results to a fusion center and disrupt the global decision-making process. In this study, we present a smart and robust defense scheme called attack-aware CSS (ACSS). The idea is based on the estimation of attack strength where the attack strength corresponds to the attack population. The attack strength is defined as the ratio of the number of malicious sensors to the total number of sensors which is equal to the probability that a specific sensor is malicious. Assuming the malicious sensor behavior or attack strategy, the proposed approach estimates attack strength and uses the Bayesian hypothesis test to improve the collaborative sensing performance. Computer simulation results illustrate the effectiveness of the proposed ACSS approach against SSDF attacks, especially for CR networks located in hostile environments.

Trust-based multi-hop cooperative spectrum sensing in cognitive radio networks

Cooperative spectrum sensing is one of the solutions for cognitive radio networks, which can resolve the uncertainty of stand-alone spectrum sensing. It means that each secondary user senses defined spectrum bands for the unused spectrum detection and shares its sensing results with the others to improve the accuracy of spectrum sensing. Due to the presence of malicious secondary users and their fake sensing reports, various forms of attacks will be encountered that reduce the performance of cooperative spectrum sensing. Spectrum sensing data falsification attack (SSDF) is one of the attacks that some research works have been presented to defend against it, based on trust and reputation management (TRM). Previous works assume that all the secondary users are in the transmission range of each other and one-hop sensing reports are provided. However, multi-hop dissemination of sensing reports is a necessary scenario for the secondary users with limited energy sources and its security attacks are very challenging to be encountered. In this paper, a trust-based multi-hop cooperative spectrum sensing method is proposed to deal with SSDF attack. Simulation results show that this scheme improves spectrum sensing accuracy and reduces.

Lightweight cloned‐node detection algorithm for efficiently handling SSDF attacks and facilitating secure spectrum allocation in CWSNs

Cognitive Wireless Sensor Networks (CWSNs) provide better bandwidth utilization when compared with normal wireless sensor networks. CWSNs use a technique called opportunistic spectrum access for data transfer. While doing so, however, CWSNs are subject to several security threats. The spectrum sensing data falsification attack comes under the DoS attack. In this attack, a malicious node sends a modified spectrum sensing report so that the resulting collaborative spectrum sensing decision becomes wrong and a good cognitive sensor node receives a wrong decision regarding the vacant spectrum band of other's network. In the presence of the node cloning attack, the solution of the SSDF attack becomes even more difficult. In the node cloning attack, the malicious node creates many clones of the compromised node in the network. In order to confuse the collaborative spectrum sensing system, the clone nodes can send false spectrum sensing reports in a large number. The maximum-match filtering (MMF) algorithm is used for making a secure spectrum sensing decision in CWSNs. The Cloned-Node Detection (CND) algorithm is proposed here to detect cloned nodes. This study also explains how the CND algorithm assists the MMF algorithm to make better spectrum sensing decisions by avoiding the node cloning attack.