Spectrum Sensing Data Falsification Research Articles

Secure Non-Consensus Based Spectrum Sensing in Non-Centralized Cognitive Radio Networks

In a non-centralized cognitive radio network, secondary users (SUs) cooperate in order to determine the presence of the primary user (PU) signal. This can be accomplished by each node sharing its spectrum sensing result with its neighboring SUs. In this paper, a non-consensus based distributed spectrum sensing (NCSS) scheme is presented, in which a node uses the raw energy level of the PU signal from its $h$ -hop neighbors for determining the status of the PU. A message passing scheme is used rather than the conventional consensus scheme. A comparison with an existing consensus algorithm is also shown. Further, two types of spectrum sensing data falsification attacks are considered in which the attackers inject very high or very low energy values. To catch such abnormal behavior of a node, an approach called secure NCSS algorithm is proposed, which isolates a node that generates extreme energy values so that it is removed from future computation. For this, an outlier detection technique is adopted. The approach is based on the idea that a malicious node’s energy level deviates significantly from those of genuine nodes, whereas all genuine nodes share nearby estimated energy levels.

Energy-Efficient Defensive Strategy Against Hybrid SSDF/Eavesdropping Attacks Over Nakagami-<inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math> </inline-formula> Channels

During cooperative spectrum sensing (CSS) in cognitive radio networks, malicious users (MUs) not only launch spectrum sensing data falsification (SSDF) attack, but also wiretap legitimate users (LUs) during the reporting phase. In this letter, an energy-efficient defensive strategy against hybrid SSDF/eavesdropping attacks is proposed, to achieve both energy-efficiency and physical layer security. Secure CSS reporting/eavesdropping channels are modeled as Nakagami- ${m}$ fading channels. The optimal MUs and LUs are determined via the achievement of maximum energy-efficiency that is subject to the optimal report bits, report distance as well as report error rate. Furthermore, intercept performance of the proposed strategy in the presence of multiple MUs is also investigated. Simulation results reveal that, the proposed strategy is immune to MU numbers, report distance as well as report error rate. Moreover, the intercept probability can be reduced in multiple MUs scenario when the legitimate-to-malicious ratio is high and the channel fading factor ratio is large.

Can Clustering be Used to Detect Intrusion During Spectrum Sensing in Cognitive Radio Networks?

Collaborative sensing helps in achieving a more accurate sensing decision than individual sensing in cognitive radio network (CRN). In an infrastructure-based CRN, each node sends its local sensing report to the fusion center (FC), which uses a fusion rule to aggregate the local sensing reports. However, collaborative sensing is vulnerable to the spectrum sensing data falsification attack, in which a node falsifies its local sensing report before sending it to the FC with the intention of disrupting the final sensing decision of the FC. In practice, the strategy of an attacker is not known. However, the collection of sensing reports at the FC can be useful for data mining with the objective of identifying the attackers. In this paper, we present a method that uses clustering techniques for detection and isolation of such attackers. We employ two clustering techniques, viz., K -medoids clustering and agglomerative hierarchical clustering. Unlike threshold detection that requires some predefined threshold value as input, the proposed approach detects the attackers using only the collection of sensing reports at the FC. We also present how we can use the proposed approach on streaming data (sensing reports), and thus, detect and isolate attackers on the fly. Comparative numerical simulation results support the validity of the approach.

An enhanced cooperative spectrum sensing scheme for anti-SSDF attack based on evidence theory

Cooperative spectrum sensing is widely accepted as an effective method to improve the reliability of spectrum sensing. However, spectrum sensing data falsification attacks (SSDF attacks) have posed serious threats to the credibility of cooperative spectrum sensing. This paper proposes an evidence theory-based anti-SSDF cooperative spectrum sensing scheme that eliminates the influence that malicious users have on the system performance in cooperative spectrum sensing. First, the data fusion center calculates the credibility according to the Jousselme distance between the evidence vectors. Then based on the credibility, the influence of malicious users on the fusion decision is reduced by using the weighted probability assignment for each cognitive user. Furthermore, to reduce the costs of collaboration and the amount of computation in applying evidence theory, the new scheme introduces a projection approximation algorithm at the local spectrum sensing process with weighted averaging in the data fusion phase. The results of the simulation show that the proposed scheme not only improves the system performance when malicious users initiate SSDF attacks, but also reduces the cooperative bandwidth and computational complexity.

Securing Cooperative Spectrum Sensing Against Collusive SSDF Attack using XOR Distance Analysis in Cognitive Radio Networks.

Cooperative spectrum sensing (CSS) is considered as a powerful approach to improve the utilization of scarce spectrum resources. However, if CSS assumes that all secondary users (SU) are honest, it may offer opportunities for attackers to conduct a spectrum sensing data falsification (SSDF) attack. To suppress such a threat, recent efforts have been made to develop trust mechanisms. Currently, some attackers can collude with each other to form a collusive clique, and thus not only increase the power of SSDF attack but also avoid the detection of a trust mechanism. Noting the duality of sensing data, we propose a defense scheme called XDA from the perspective of XOR distance analysis to suppress a collusive SSDF attack. In the XDA scheme, the XOR distance calculation in line with the type of “0” and “1” historical sensing data is used to measure the similarity between any two SUs. Noting that collusive SSDF attackers hold high trust value and the minimum XOR distance, the algorithm to detect collusive SSDF attackers is designed. Meanwhile, the XDA scheme can perfect the trust mechanism to correct collusive SSDF attackers’ trust value. Simulation results show that the XDA scheme can enhance the accuracy of trust evaluation, and thus successfully reduce the power of collusive SSDF attack against CSS.

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

SummarySpectrum 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 (MUPUE) 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 (MUSSDF) 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.

Mitigation strategy against SSDF attack for healthcare in cognitive radio networks

Wireless communications have a rapid growth that leads to huge demand on the deployment of new wireless services in both licensed and unlicensed frequency spectrum. The performance of Cooperative Spectrum Sensing (CSS) in cognitive radio is based on two factors: cooperative gain and cooperative overhead. There are many other dominating factors that affect the performance of CSS like the presence of attacks, energy efficiency, sensing time and delay, spectrum efficiency, etc. The major threat to CSS is Spectrum Sensing Data Falsification (SSDF) attack. Existing methods to mitigate the SSDF attack doesn't focus on energy efficiency which is performed here. To deal with these challenges, we propose an energy efficient mechanism against SSDF attack for healthcare application in Cognitive Radio Networks (CRNs). We propose a Malicious Node Identification Algorithm (MIA) to mitigate the problem of SSDF attack in the network and to eliminate the redundant data at the fusion centre, Balanced Cluster-based Redundancy Check Aggregation (BCRCA) mechanism is proposed. Finally, the decision taken by the fusion centre is based on Weighted Selection Combining (WSC) technique that improves spectrum decision accuracy.

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

Cognitive Wireless Sensor Networks (CWSNs) provide better bandwidth utilisation compared to normal Wireless Sensor Networks (WSNs) by using opportunistic spectrum access to transfer data. They transmit data through the primary user's spectrum band when there is heavy traffic in its own network, thereby eliminating collisions and delays in data delivery. During this, CWSNs are subject to several security threats, attacks on secrecy and authentication, attacks on network availability (DOS attacks), stealth attacks on service integrity etc. The Spectrum Sensing Data Falsification (SSDF) attack is a type of DOS attack where the attackers modify the spectrum sensing report to compel the base station to take a wrong collaborative decision regarding the vacant spectrum band in other networks. In this paper, we have proposed the Maximum-Match Filtering algorithm (MMF) for collaborative spectrum sensing and spectrum decision making in CWSNs, which is executed at the base station to counter the SSDF attack.

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

In cognitive radio networks, cooperative spectrum sensing (CSS) is generally adopted for improving spectrum sensing accuracy to increase spectrum utilization and avoid interference with the primary users. However, some malicious secondary users (SUs) may affect the CSS performance by inducing false observation bits for fusion. The message authentication code (MAC) is a promising technique to avoid the damage from the spectrum sensing data falsification (SSDF) attacks. In this paper, as both the more spectrum sensing nodes and the MAC reporting bits result in extra energy consumption, we propose an energy efficiency model to capture the effects of the length of MAC and the number of cooperative SUs under independent and collaborative SSDF attacks, respectively, and analyze the existence of the optimal length of MAC and the optimal number of cooperative SUs that can achieve the maximum value of energy efficiency, respectively. Simulation results are provided to show that the CSS scheme based on MAC can resist SSDF attacks and the accuracy of the theoretical analysis is also validated.

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.

A Context-Aware Trust Framework for Resilient Distributed Cooperative Spectrum Sensing in Dynamic Settings

Cognitive radios enable dynamic spectrum access where secondary users (SUs) are allowed to operate on the licensed spectrum bands on an opportunistic noninterference basis. Cooperation among the SUs for spectrum sensing is essential for environments with deep shadows. In this paper, we study the adverse effect of insistent spectrum sensing data falsification (ISSDF) attack on iterative distributed cooperative spectrum sensing. We show that the existing trust management schemes are not adequate in mitigating ISSDF attacks in dynamic settings where the primary user (PU) of the band frequently transitions between active and inactive states. We propose a novel context-aware distributed trust framework for cooperative spectrum sensing in mobile cognitive radio ad hoc networks (CRAHN) that effectively alleviates different types of ISSDF attacks (Always-Yes, Always-No, and fabricating) in dynamic scenarios. In the proposed framework, the SU nodes evaluate the trustworthiness of one another based on the two possible contexts in which they make observations from each other: PU absent context and PU present context. We evaluate the proposed context-aware scheme and compare it against the existing context-oblivious trust schemes using theoretical analysis and extensive simulations of realistic scenarios of mobile CRAHNs operating in TV white space. We show that in the presence of a large set of attackers (as high as 60% of the network), the proposed context-aware trust scheme successfully mitigates the attacks and satisfy the false alarm and missed-detection rates of $10^{-2}$ and lower. Moreover, we show that the proposed scheme is scalable in terms of attack severity, SU network density, and the distance of the SU network to the PU transmitter.

Design of a novel dynamic trust model for spectrum management in WRANs of TV white space

The cognitive radio (CR) technique is considered as a better and more efficient answer to the predicament brought by the high demands of wireless communication services and the scarcity or low utilization of frequency spectrum resources. The IEEE 802.22 standard has regulated the wireless regional area networks (WRANs) as one example of the CR networks, which is operating in the television white space (TVWS) spectrum. In the centralized WRANs, the secondary user base station (SUBS) allocates the spectrum holes for the customer premise equipment (CPE) on the basis of the spectrum sensing information from the CPEs around. However, since the CPEs are easy compromised under the varying and uncontrollable environment, the WRANs is subjected to the primary user emulation attacks (PUEA) and the spectrum sensing data falsification (SSDF) attacks, which may lead to a wrong global spectrum decision made by SUBS. A novel dynamic trust model for the SUBS spectrum management based on the subjective logic for the centralized WRANs of TVWS is proposed in this paper. The proposed trust model has a positive impact on reducing the influence of users’ vicious behaviors and encourages the CPEs to participate in truthful actions for communications. The analysis shows that the time complexity of the proposed trust model is O(n). Simulations indicate that the proposed trust model can significantly reduce the probability of the errors by the SUBS in the spectrum decision making.

History based forward and feedback mechanism in cooperative spectrum sensing including malicious users in cognitive radio network.

In cognitive radio communication, spectrum sensing plays a vital role in sensing the existence of the primary user (PU). The sensing performance is badly affected by fading and shadowing in case of single secondary user(SU). To overcome this issue, cooperative spectrum sensing (CSS) is proposed. Although the reliability of the system is improved with cooperation but existence of malicious user (MU) in the CSS deteriorates the performance. In this work, we consider the Kullback-Leibler (KL) divergence method for minimizing spectrum sensing data falsification (SSDF) attack. In the proposed CSS scheme, each SU reports the fusion center(FC) about the availability of PU and also keeps the same evidence in its local database. Based on the KL divergence value, if the FC acknowledges the user as normal, then the user will send unified energy information to the FC based on its current and previous sensed results. This method keeps the probability of detection high and energy optimum, thus providing an improvement in performance of the system. Simulation results show that the proposed KL divergence method has performed better than the existing equal gain combination (EGC), maximum gain combination (MGC) and simple KL divergence schemes in the presence of MUs.

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.