Spectrum Sensing Data Falsification Attack Research Articles

Cooperative Estimation of Primary Traffic Under Imperfect Spectrum Sensing and Byzantine Attacks

Cognitive radio (CR) systems exploit the accurate knowledge of primary traffic statistics to improve the CR performance and reduce the harmful interference on primary network. It is essential for cooperative CRs to operate in an efficient secured manner while estimating the primary statistics. In this paper, a new reporting mechanism for cooperative estimation of primary traffic is proposed to increase the spectrum and energy efficiency. This is achieved by the reduction of the reporting channel overhead from cooperative users to the fusion center. Simulation results show that the proposed scheme reduces significantly the signaling overhead, thus making the system more spectrum and energy efficient. Moreover, the openness of cooperative CR makes it susceptible to data falsification attacks, also known as Byzantine attacks. This attack poses a series of damages on the reliability of the estimation of primary traffic. In this paper, we define the types of malicious coordinated attacks on CRs and analyze the possibility of estimating the primary traffic statistics under these attacks. Moreover, we provide a simple yet effective countermeasure based on the proposed reporting for cooperative estimation. Simulation evaluation shows that the proposed algorithm provides an excellent countermeasure for spectrum sensing data falsification attacks.

Maximum match filtering algorithm to defend spectrum-sensing data falsification attack in CWSN

Maximum match filtering algorithm to defend spectrum-sensing data falsification attack in CWSN

Honest Auction Based Spectrum Assignment and Exploiting Spectrum Sensing Data Falsification Attack Using Stochastic Game Theory in Wireless Cognitive Radio Network

Cognitive radio networks being a widespread disruptive technology where idle spectrum are being analysed and made available to the unlicensed secondary users (SU) from licensed primary users (PU). During this battle of allocation of spectrum to the SU, the attackers can masquerade the network and disrupt the channels. The vulnerability attacks are more often happened based on the nature of usage of the spectrum in PU and they collect those information from secondary agent cluster, one among them is the Spectrum Sensing data falsification attack. In heterogeneous network, the vulnerabilities affect the utility of the channel and changes the information in fusion centre, where the sensing reports are might be corrupted during transmission. The paper focus on assigning the idle spectrum to be allocated based on binary stage honest based winner determination using greedy approach and the broadcasted information is secured using two phase cryptography. Besides, a rational player approach has been proposed to make the network to be susceptible to the idle environment using stochastic game modeling to prevent the attack, where a optimal solution is being estimated with nash equilibrium to identify the best response for the user to exploit the attack. The proposed mechanism is much more significantly improved duly compared with traditional mechanism and it highly improves the network performance in monitoring the induced attack and allocating the spectrum with error free transmission.

Trust-based mechanism design for cooperative spectrum sensing in cognitive radio networks

We propose and analyze trust-based cooperative spectrum sensing data fusion schemes against spectrum sensing data falsification attacks in cognitive radio networks. We first consider the case in which a centralized data fusion center is in place for decision making. Then we extend it to the case in which the data fusion center is absent leading to autonomous and distributed decision making. Our trust-based data fusion schemes are based on mechanism design theoryto motivate users to report authentic sensing data so as to improve the success rate. Further, we decouple erroneous sensing reports due to low sensing capabilities from false reports due to attacks, thus avoiding unnecessary punishments to good users. We conduct a theoretical analysis validated with extensive simulation and identify optimal parameter settings under which our trust-based data fusion schemes outperform existing non-trust based cooperative spectrum sensing data fusion schemes.

Protection Against Defecting Attack and Enhanced Channel Allocation for Video Streaming in CRN

Arising issue of spectrum scarcity is resolved by emerging cognitive radio technology. In which, unoccupied portion of licensed spectrum would be granted for unlicensed user in order to upgrade the spectrum utilization. Spectrum sensing is an enabling task for labeling the spectrum holes and away from spectrum congestion in cognitive radio networks. Impact of secondary user’s cooperation developed cooperative spectrum sensing for effective spectrum sensing, many secondary users are to be associated in sensing the idle channels. Adversary secondary users (SU) mimic as primary user in cooperative sensing scheme, called primary user emulation attack (PUEA). If any SU forges the sensing data, that is known as spectrum sensing data falsification attack (SSDF). Rescuing the network against these two attacks is the major issue of cognitive radio networks. To address these challenges, we propose cognitive radio network to ensure security in cooperative spectrum sensing for video streaming application. Sensing based clustering algorithm assembles the cognitive radios into clusters to mitigate cooperative overhead and consumed energy. In order to avert PUEA attacks, we introduce authentication signature ID algorithm which provides authority for licensed user to use spectrum. To diminish SSDF attacks, we exploit Hamming algorithm to abolish forgery node from spectrum sensing decision. Furthermore PRN channel allocation algorithm is employed to precisely deliver the video with high quality and lower delay. For video transmission in cognitive radio network, we need to compress the video frames by block truncation coding-pattern fitting in order to reduce bandwidth consumption. This compression technique offers good video transmission with high compression ratio in cognitive radio network, due to trade-off among quality, bit rate and decoding time. Our proposed framework demonstrates the simulation with improved detection rate of idle channel and reduced the delay for high quality video transmission.

Cooperative Spectrum Sensing With M-Ary Quantized Data in Cognitive Radio Networks Under SSDF Attacks

In this paper, we address the challenging and important cooperative spectrum sensing (CSS) problem with M-ary quantized data under spectrum sensing data falsification (SSDF) attacks. We introduce a probabilistic SSDF attack model to characterize the attacks by a malicious secondary user (SU). We analyze the attack behavior and derive the condition to nullify the detection capability of the fusion center (FC). To defend against the SSDF attacks, we propose a novel attack-proof CSS scheme with M-ary quantized data, mainly including a malicious SU identification method and an adaptive linear combination rule. By using the malicious SU identification approach, FC identifies malicious SUs and removes them from the data fusion process. The adaptive linear combination rule adjusts the weighted coefficients with the distribution parameter sets of identified normal SUs estimated using a maximum likelihood-based estimator. FC performs the spectrum sensing process with M-ary quantized data from the identified normal SUs. Comprehensive evaluation is conducted. Evaluation results show that the proposed malicious SU identification method can remove malicious SUs successfully and the proposed CSS scheme with M-ary quantized data is robust against the SSDF attacks.

Defeating SSDF Attacks With Trusted Nodes Assistance in Cognitive Radio Networks

Spectrum sensing data falsification (SSDF) is one of the most dangerous attacks that can degrade the reliability of collaborative spectrum sensing in cognitive radio (CR) networks. In this article, we propose a novel defense strategy to thwart SSDF attacks by intelligently verifying sensory data with the assistance of trusted nodes. The proposed scheme employs an efficient and fast reputation-based algorithm to analyze the behavior of each user. Hence, not only will reliable sensory data be accepted by the central entity, but also, malicious users can be easily identified and removed from the network. Both theoretical and simulation results show that the proposed method provides a powerful countermeasure against SSDF attacks in an adversarial CR environment.

A Multi-factor Trust Management Scheme for Secure Spectrum Sensing in Cognitive Radio Networks

Cognitive radio (CR) is an emerging technology for efficient utilization of the limited and scarce radio spectrum resources. Spectrum sensing is a key task in CR system for detecting the unused radio spectrum or spectrum holes by the primary users. In this regard, collaborative spectrum sensing (CSS) has shown to as an efficient way to improve such spectrum holes detection in cognitive radio network (CRN). However, this cooperative nature makes it vulnerable to many types of security attacks. In this paper, we focus one of these potential CRN specific security attack called spectrum sensing data falsification attack or Byzantine attack. In which the malicious internal member of the network, reports false sensing results with a aim to increase the sensing errors. In order to ensure the security requirements of CRN, we proposed a novel trust management mechanism that evaluates the trustworthiness of each node participating in the CSS scheme called sensing reputation (SR). To reflect the complexity, the SR calculation involves multiple decision factors like history based trust factor, active factor, incentive factor and consistency factor. Based on this SR value, the suspicious users are identified and the malicious users are filtered out from the decision making process of the CSS scheme. We further introduce the concept of sensing reputation chain to record and track the future behavior of the identified suspicious users. Theoretical analysis and simulation results demonstrate the effectiveness of our proposed malicious user detection technique with a higher accuracy and lower false alarm rate.

Using Stackelberg game to enhance cognitive radio sensor networks security

The authors propose a game-theoretic approach using the Stackelberg game for securing cognitive radio sensor network (CRSN) against the spectrum sensing data falsification attack; this attack aims at corrupting the spectrum decisions communicated from the ambient sensor nodes (ASNs) to the fusion centre by imposing interference power. The proposed game approach is developed for two different attack–defence scenarios. In the first scenario, the attacker selects to attack a group of delivered reports of the ASNs that have a protection degree below a specific threshold. In the second scenario, the attacker applies its maximum attack interference power to the delivered reports of the ASNs that have been reported to be least protected in the previous round. Simulation results indicate the improved performance of the proposed protection model as compared with two baseline defence mechanisms, namely, the random and equal-protection defence mechanisms with static signal-to-noise ratio (SNR) and variable SNRs. Consequently, Stackelberg game features prove to be beneficial for securing communication over CRSN.

Securing multi-channel selection using distributed trust in cognitive radio ad hoc networks

Cognitive radio ad hoc networks (CRAHNs) which are independent with infrastructure and fixed spectrum allocation policy, have been developed rapidly in the environment of scarce spectrum resources. However, the nature of multi-channel makes CRAHNs offers opportunities for malicious SUs. Cooperative spectrum sensing (CSS) is the key function of CRAHNs to identify the idle spectrum as the available channel by aggregating sensing data. Malicious SUs can launch spectrum sensing data falsification (SSDF) attack against CSS, and thus causing the disturbance of finding multi-channel. Recently, a lot of efforts with trust schemes have been paid to combating SSDF attack in central cognitive radio environments, but little attention to the distributed environments. In this paper, we propose a distributed trust evaluation (DTE) scheme from the perspective of direct experience and neighbor help to counter SSDF attack, which can be implemented at each SU in CRAHNs rather than a central authority. To secure multi-channel selection fully, it is very important to avoid the conflict among SUs for competing idle channels. We also design a distributed trust implementation protocol to manage CSS with the DTE scheme in a distributed manner. Simulation results show that our scheme can successfully suppress SSDF attack at a low cost, and thus ensure the security of multi-channel selection in CRAHNs.

Probabilistic spectrum sensing data falsification attack in cognitive radio networks

Cognitive radio has emerged as a solution to the problem of spectrum scarcity in recent years. Spectrum sensing is a key task in cognitive radio systems that can be performed collaboratively. Although collaboration enhances the performance of spectrum sensing, it can put the cognitive network in a vulnerable position. The cognitive users may send falsified reports, hence degrade the performance of spectrum sensing. In this paper, we propose a flexible structure which enables the attacker to reconfigure the attack parameters based on the defense strategy employed at the fusion center (FC) adaptively. In particular, we consider a cognitive network in which the cognitive users send their observations in a quantized format. It is assumed that a soft-decision-based defense strategy is employed at FC to detect the attackers. The attacker maps its quantized observations onto the other quantization levels probabilistically and reports them to FC. The attacker’s objective is to perform probabilistic mapping such that the performance of FC degrades as much as possible. At the same time, the attacker considers the attack costs. We demonstrate that the proposed attack method leads to a convex linear programming problem. A low complexity algorithm is proposed to solve the problem. Simulation results are presented to show the effectiveness of the proposed scheme.

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.

Performance of p-Norm Detector in Cognitive Radio Networks with Cooperative Spectrum Sensing in Presence of Malicious Users

A cognitive radio network (CRN) with a cooperative spectrum sensing scheme is considered. This CRN has a primary user and multiple secondary users, some of which are malicious secondary users (MSUs). Energy detection at each SU is performed using a p-norm detector with p≥2, where p=2 corresponds to the standard energy detector. The MSUs are capable of perpetrating spectrum sensing data falsification (SSDF) attacks. At the fusion center (FC), an algorithm is used to suppress these MSUs which could be either an adaptive weighing algorithm or one of the following: Tietjen-Moore (TM) test or Peirce’s criterion. This is followed by computation of a test statistic (TS) which is a random variable. In this paper, we assume TS to have either a Gamma or a Gaussian distribution and calculate the threshold accordingly. We provide closed-form expressions of probability of false alarm and probability of miss-detection under both assumptions. We show that Gaussian assumption of TS is more suited in presence of an SSDF attack when compared with the Gamma assumption. We also compare the detection performance for various values of p and show that p=3 along with the Gaussian assumption is the best amongst all the cases considered.

Comprehensive Reputation-Based Security Mechanism against Dynamic SSDF Attack in Cognitive Radio Networks

Collaborative spectrum sensing (CSS) was envisioned to improve the reliability of spectrum sensing in centralized cognitive radio networks (CRNs). However, secondary users (SUs)’ changeable environment and ease of compromise make CSS vulnerable to security threats, which further mislead the global decision making and degrade the overall performance. A popular attack in CSS is the called spectrum sensing data falsification (SSDF) attack. In the SSDF attack, malicious cognitive users (MUs) send false sensing results to the fusion center, which significantly degrades detection accuracy. In this paper, a comprehensive reputation-based security mechanism against dynamic SSDF attack for CRNs is proposed. In the mechanism, the reliability of SUs in collaborative sensing is measured with comprehensive reputation values in accordance with the SUs’ current and historical sensing behaviors. Meanwhile a punishment strategy is presented to revise the reputation, in which a reward factor and a penalty factor are introduced to encourage SUs to engage in positive and honest sensing activities. The whole mechanism focuses on ensuring the correctness of the global decision continuously. Specifically, the proposed security scheme can effectively alleviate the effect of users’ malicious behaviors on network decision making, which contributes greatly to enhancing the fairness and robustness of CRNs. Considering that the attack strategy adopted by MUs has been gradually transforming from simplicity, fixedness and singleness into complexity, dynamic and crypticity, we introduce two dynamic behavior patterns (true to false and then to true (TFT) and false to true and then to false (FTF)) to further validate the effectiveness of our proposed defense mechanism. Abundant simulation results verify the rationality and validity of our proposed mechanism.

Reputation based Proposed Scheme to Ensure Reliable Decision by Fusion Centre

Objectives: Dynamic spectrum access is the promising feature of Cognitive Radio (CR). When Spectrum sensing is executed in a cooperative manner, shadowing and fading get reduced. Therefore, it gives an invitation to security threats. Methods/Statistical Analysis: This paper elaborates various security hazards in cognitive radio networks (CRN) along with their countermeasures. The paper elucidates the complication of Spectrum Sensing Data Falsification (SSDF) attack. The Primary objective of SSDF attack is to devastate the judgement of Fusion Centre (FC). With the aim to mitigate the influence of adversaries on the judgement given by FC, this paper proposes a scheme based on reputation and trust of cognitive users. Findings: Proposed scheme works on a well-defined strategy that will annihilate the impact of malicious users. In this FC doesn’t possess any knowledge about the number of adversaries and strategy of attack. In order to minimize the influence of adversary, implementation of clusters is considered to be a good idea. Cluster formation takes place according to specified criteria. Encryption on the sensing reports of valid clusters is exercised, with the aim to prevent from attacks such as eavesdropping etc. Parameters such as false positive rate and true detection rate can use to judge the performance of the proposed scheme. Applications/Improvements: SSDF attack is the most dangerous attack and proposed scheme not only mitigates the effect of SSDF attack but also stands strong against other dangerous attacks such as eavesdropping attack.

Joint Spectrum Sensing and Resource Allocation Scheme in Cognitive Radio Networks with Spectrum Sensing Data Falsification Attack

Spectrum sensing and spectrum sharing are two fundamental issues in a cognitive radio network (CRN). The spectrum sensing data falsification (SSDF) attack imposes bad effects on both spectrum sensing and spectrum sharing. To deal with SSDF attacks and to incentivize secondary users (SUs) to behave well, a joint spectrum sensing and resource allocation (JSSRA) scheme in a CRN is proposed in this paper. The JSSRA problem is formulated as a weighted-proportional-fairness-based resource-allocation optimization problem under the constraint that the primary user (PU) network is sufficiently protected. The problem is decomposed into two subproblems, namely, the resource allocation subproblem and cooperative SU number decision subproblem, which are solved by the Lagrangian dual algorithm and the brute-force algorithm, respectively. Moreover, a user-selection method based on reinforcement learning is presented to select reliable SUs for cooperative spectrum sensing (CSS). In addition, the SU's trust degree is updated according to its behavior in CSS and is used in the sensed resource-allocation process. The key point of our proposed scheme is to make SUs improve the sensing reliability and prevent SUs from behaving maliciously by an incentive mechanism. Comprehensive performance evaluation is conducted by computer simulation. It is shown that the proposed JSSRA scheme deals with the SSDF attack well in cooperative sensing process to improve system robustness and achieves a significant system utility gain in resource allocation.

Secure Channel Allocation Approach for Video Streaming in Cognitive Radio Networks

In this paper, we propose a technique to ensure security in cooperative spectrum sensing for video streaming application in cognitive radio network. Sensing based clustering algorithm puts together the cognitive radios into clusters to mitigate cooperative overhead and to reduce energy consumption. In order to avert primary user emulation attacks, we introduce authentication signature ID algorithm which provides authority for the licensed user to use the spectrum. To diminish spectrum sensing data falsification attacks, we exploit hamming algorithm to expel forgery node from spectrum sensing decision. Furthermore, the primary radio network channel allocation algorithm is employed to precisely deliver the video with high quality and lower delay. For video transmission in a cognitive radio network, we compress the video frames using block truncation coding-pattern fitting in order to reduce bandwidth requirement. This compression technique offers good video transmission with a high compression ratio in a cognitive radio network, due to trade-off among quality, bit rate and decoding time. Simulation results reveal that there is a significant improvement in the detection rate of idle channel and reduction of delay for high-quality video transmission.

Exploiting Secure and Energy-Efficient Collaborative Spectrum Sensing for Cognitive Radio Sensor Networks

Cognitive radio sensor network (CRSN) has emerged as a promising solution to address the spectrum scarcity problem in traditional sensor networks, by enabling sensor nodes to opportunistically access licensed spectrum. To protect the transmission of primary users and enhance spectrum utilization, collaborative spectrum sensing is generally adopted for improving spectrum sensing accuracy. However, as sensor nodes may be compromised by adversaries, these nodes can send false sensing reports to mislead the spectrum sensing decision, making CRSNs vulnerable to spectrum sensing data falsification (SSDF) attacks. Meanwhile, since the energy consumption of spectrum sensing is considerable for energy-limited sensor nodes, SSDF attack countermeasures should be carefully devised with the consideration of energy efficiency. To this end, we propose a secure and energy-efficient collaborative spectrum sensing scheme to resist SSDF attacks and enhance the energy efficiency in CRSNs. Specifically, we theoretically analyze the impacts of two types of attacks, i.e., independent and collaborative SSDF attacks, on the accuracy of collaborative spectrum sensing in a probabilistic way. To maximize the energy efficiency of spectrum sensing, we calculate the minimum number of sensor nodes needed for spectrum sensing to guarantee the desired accuracy of sensing results. Moreover, a trust evaluation scheme, named FastDtec, is developed to evaluate the spectrum sensing behaviors and fast identify compromised nodes. Finally, a secure and energy-efficient collaborative spectrum sensing scheme is proposed to further improve the energy efficiency of collaborative spectrum sensing, by adaptively isolating the identified compromised nodes from spectrum sensing. Extensive simulation results demonstrate that our proposed scheme can resist SSDF attacks and significantly improve the energy efficiency of collaborative spectrum sensing.

Identification and Punishment Policies for Spectrum Sensing Data Falsification Attackers Using Delivery-Based Assessment

Spectrum sensing data falsification (SSDF) attacks represent a major challenge for cooperative spectrum sensing (CSS) in cognitive radio (CR) networks. In an SSDF attack, a malicious user or many malicious users send false sensing results to the fusion center (FC) to mislead the global decision about spectrum occupancy. Thus, an SSDF attack degrades the achievable detection accuracy, throughput, and energy efficiency of CR networks (CRNs). In this paper, a novel attacker-identification algorithm is proposed that is able to skillfully detect attackers and reject their reported results. Moreover, we provide a novel attacker-punishment algorithm that aims at punishing attackers by lowering their individual energy efficiency, motivating them either to quit sending false results or leave the network. Both algorithms are based on a novel assessment strategy of the sensing performance of each user. The proposed strategy is called delivery-based assessment, which relies on the delivery of the transmitted data to evaluate the made global decision and the individual reports. Mathematical analysis and simulation results show promising performance of both algorithms compared with previous works, particularly when then the number of attackers is very large.

Pre‐processing of incomplete spectrum sensing data in spectrum sensing data falsification attacks detection: a missing data imputation approach

Attacks detection is an important issue in collaborative spectrum sensing (CSS) system of cognitive radio networks. Many approaches are proposed to cope with the malicious behaviour of attackers. In existing works, spectrum sensing data (SSD) received by the fusion centre is generally assumed to be integrated. However, in practical scenarios, the received SSD may be incomplete due to the imperfect reporting channel or specific CSS schemes. The performance of existing attacks detection approaches may degrade especially when the probability of missing data is large. To alleviate this challenge, the authors focus on pre-processing of incomplete SSD and propose a practical imputation algorithm, which is derived from the maximum a posteriori probability criterion, to fill in the missing values. Simulation results indicate that the proposed algorithm restores the characteristics of the SSD, and mitigate the impacts of missing value on existing attacks detection algorithm effectively.