Malicious Secondary Users Research Articles

Joint spectrum sensing and D2D communications in Cognitive Radio Networks using clustering and deep learning strategies under SSDF attacks

In Spectrum Sensing and Data Falsification Attacks (SSDF), malicious Secondary Users (SUs) in Cognitive Radio Networks (CRNs) intentionally try to disrupt the global Cooperative Spectrum Sensing (CSS) decision for their self-benefit. Most existing works focus on mitigating the impact of SSDF attacks on CSS decisions. However, a small piece of work jointly studies the CSS and opportunistic data transmission under SSDF attacks in a single framework, but they have some limitations. The present work proposes joint CSS and SU data transmissions in Energy Harvesting-enabled CRNs under SSDF attacks. The present work uses the clustering strategy to isolate the malicious SUs from the honest set of SUs using reputation value and other attributes. The identified malicious and unfit SUs are restricted from opportunistic Device-to-Device (D2D) communication in CRNs. An Ensemble Learning strategy is proposed, which enhances the CSS reliability over the existing works by ∼36.91%, ∼25.00%, and ∼19.04%. Several network constraints guide the reliable SU transmissions in a hybrid framework under SSDF attacks to support the Quality-of-Services for both primary and secondary networks.

Defense against SSDF Attack and PUE Attack in CR-Internet of Vehicles (IoVs) for Millimeter Wave Massive MIMO Beamforming Systems

The Internet of Vehicles (IoV) is witnessed to play the leading role in the future of Intelligent Transportation Systems (ITS). Though many works have focused on IoV improvement, there is still a lack of performance due to insufficient spectrum availability, lower data rates, and the involvement of attackers. This paper considers all three issues by developing a novel mmWave-assisted Cognitive Radio based IoV (CR-IoV) model. The integration of CR in IoV resolves the issue of spectrum management, while mmWave technology ensures symmetry in acquiring higher data rates for Secondary Users (SUs). With the proposed mmWave-assisted CR-IoV model, symmetric improvements in network performance were achieved in three main areas: security, beamforming, and routing. Optimum detection mechanisms isolate malicious Secondary Users (SUs) in the overall network. First, Spectrum Sensing Data Falsification (SSDF) attack is detected by a Hybrid Kernel-based Support Vector Machine (HK-SVM), which is the lightweight Machine Learning (ML) technique. Then, the Primary User Emulation (PUE) attack is detected by a hybrid approach, namely the Fang Algorithm-based Time Difference of Arrival (FA-TDoA) method. Further, security is assured by validating the legitimacy of each SU through a Lightweight ID-based Certificate Validation mechanism. To accomplish this, we employed the Four Q-curve asymmetric cryptographic algorithm. Overall, the proposed dual-step security provisioning approach assures that the network is free from attackers. Next, beamforming is performed for legitimate SUs by a 3D-Beamforming algorithm that relies on Array Factor (AF) and Beampattern Function. Finally, routing is enabled by formulating Forwarding Zone (FZ) based on the forwarding angle. In the forwarding zone, optimal forwarders are selected by the Multi-Objective Whale Optimization (MOWO) algorithm. Here, a new potential score is formulated for fitness evaluation. Finally, the proposed mmWave-assisted CR-IoV model is validated through extensive simulations in the ns-3.26 simulation tool. The evaluation shows better performance in terms of throughput, packet delivery ratio, delay, bit error rate, and detection accuracy.

Deep Reinforcement Learning based reliable spectrum sensing under SSDF attacks in Cognitive Radio networks

In Spectrum Sensing Data Falsification (SSDF) attacks, malicious Secondary Users (SUs) actively send erroneous local sensing results to the Fusion Centre (FC) that influence the actual outcomes of Cooperative Spectrum Sensing (CSS). Existing trust value-based algorithms are partially successful as SUs can quickly change their characteristics from honest to malicious and vice versa. The present work explores the Deep Reinforcement Learning algorithm (DRL) for adapting the behavioural changes of SUs during CSS to overcome the issue mentioned earlier. The agent in the DRL algorithm wisely avoids the malicious data from the received sensed-energy values at FC and reduces the sensing error effectively. Simulation results show that the proposed work outperforms the conventional and Support Vector Machine-based approaches in CSS reliabilities under SSDF attacks.

Joint Spectrum Sensing and Spectrum Access for Defending Massive SSDF Attacks: A Novel Defense Framework

Multiple secondary users (SUs) perform collaborative spectrum sensing (CSS) in cognitive radio networks to improve the sensing performance. However, this system severely degrades with spectrum sensing data falsification (SSDF) attacks from a large number of malicious secondary users, i.e., massive SSDF attacks. To mitigate such attacks, we propose a joint spectrum sensing and spectrum access framework. During spectrum sensing, each SU compares the decisions of CSS and independent spectrum sensing (IndSS), and then the reliable decisions are adopted as its final decisions. Since the transmission slot is divided into several tiny slots, at the stage of spectrum access, each SU is assigned with a specific tiny time slot. In accordance with its independent final spectrum decisions, each node separately accesses the tiny time slot. Simulation results verify effectiveness of the proposed algorithm.

Identifying Malicious Secondary User Presence Within Primary User Range in Cognitive Radio Networks

Cognitive Radio (CR) Network is a wireless communication technology, in which a detection device smartly detects occupied and unoccupied channels. During traffic, unused primary user spectrum space is allotted to a secondary user, without causing any intervention with the primary user. Though this has reduced network traffic to a great extent, many issues related to security has became an alarming problem, in which the primary user’s space is misused by some malicious secondary users without the knowledge of primary user. To address this issue, in this paper we have proposed a Boundary detection method that uses the estimated location of each SU, which is obtained using the Recurrent Neural Network algorithm, to determine the boundary of PU coverage. Then Malicious User Detection by Ordering (MUDO) methodology is proposed, in which all secondary users are weighed using Basic Probability Analysis (BPA), and based on the orders the SUs are paired with corresponding PUs. The SUs with the least orders are discarded as they might be malicious users. The proposed methodology possesses higher detection speed and precise detection thereby enhancing the performance of CR.

Bilateral Privacy-Utility Tradeoff in Spectrum Sharing Systems: A Game-Theoretic Approach

In spectrum sharing systems based on spectrum trading, user locations are vital for the efficiency of dynamic channel reuse. However, both primary users (PUs) and secondary users (SUs) undertake the risk of location information leakage: a malicious PU may illegally collect SUs' location information to manipulate market decisions; a malicious SU would threat a PU's operational privacy by inferring the PU's location through seemingly inoffensive queries. To protect both PUs' and SUs' location information in spectrum trading, a bilateral privacy preservation framework is introduced in this paper. A game-theoretic approach based on the Stackelberg model is proposed to achieve the tradeoff between the privacy-preserving level and user utility. With the proposed approach, both PUs and SUs can maximize their utilities while maintaining their location privacy to desired levels. Simulation results demonstrate that the proposed approach can effectively enhance user utility gain and strengthen user privacy guarantee by flexibly adjusting their privacy levels in practice.

Fuzzy-based reliable and secure cooperative spectrum sensing for the smart grid

Cognitive radio is a promising technology to solve the spectrum scarcity problem caused by inefficient utilization of radio spectrum bands. It allows secondary users to opportunistically access the underutilized spectrum bands assigned to licensed primary users. The local individual spectrum detection is inefficient, and cooperative spectrum sensing is employed to enhance spectrum detection accuracy. However, cooperative spectrum sensing opens up opportunities for new types of security attacks related to the cognitive cycle. One of these attacks is the spectrum sensing data falsification attack, where malicious secondary users send falsified sensing reports about spectrum availability to mislead the fusion center. This internal attack cannot be prevented using traditional cryptography mechanisms. To the best of our knowledge, none of the previous work has considered both unreliable communication environments and the spectrum sensing data falsification attack for cognitive radio based smart grid applications. This paper proposes a fuzzy inference system based on four conflicting descriptors. An attack model is formulated to determine the probability of detection for both honest and malicious secondary users. It considers four independent malicious secondary users’ attacking strategies of always yes, always no, random, and opposite attacks. The performance of the proposed fuzzy fusion system is simulated and compared with the conventional fusion rules of AND, OR, Majority, and the reliable fuzzy fusion that does not consider the secondary user’s sensing reputation. The results indicate that incorporating sensing reputation in the fusion center has enhanced the accuracy of spectrum detection and have prevented malicious secondary users from participating in the spectrum detection fusion

Mitigation of the spectrum sensing data falsifying attack in cognitive radio networks

ABSTRACT Cognitive radio networks (CRNs), which offer novel network architecture for utilising spectrums, have attracted significant attention in recent years. CRN users use spectrums opportunistically, which means they sense a channel, and if it is free, they start transmitting in that channel. In cooperative spectrum sensing, a secondary user (SU) decides about the presence of the primary user (PU) based on information from other SUs. Malicious SUs (MSUs) send false sensing information to other SUs so that they make wrong decisions about the spectrum status. As a result, an SU may transmit during the presence of the PU or may keep starving for the spectrum. In this paper, we propose a reputation-based mechanism which can minimise the effects of MSUs on decision making in cooperative spectrum sensing. Some of the SUs are selected as distributed fusion centres (DFCs), that are responsible for making decisions about the presence of PU and informing the reporting SUs. A DFC uses weighted majority voting among the reporting SUs, where weights are normalised reputation. The DFC updates reputations of SUs based on confidence of an election. If the majority wins by a significant margin, the confidence of the election is high. In this case, SUs that belong to the majority gain high reputations. We conduct extensive simulations to validate our proposed model.

Defense Against Spectrum Sensing Data Falsification Attacker in Cognitive Radio Networks

Cognitive radio is an empowering innovation that guarantees to achieve spectrum utilization. To manage the muddled wireless condition, cooperative spectrum sensing (CSS) has been proposed to make use of the assorted variety in cognitive radio networks (CRNs). However, due to the simplicity of CRNs, CSS are vulnerable to security threats. Spectrum sensing data falsification (SSDF) attacker is one of the attackers in CSS. In the SSDF attack, malicious secondary users (MSUs) send false sensing decisions to the fusion center, which significantly degrades detection accuracy. In this paper, to identify MSUs, a modified delivery based scheme is proposed, where a minimum number of samples are considering for sensing. The proposed security scheme is shown to successfully alleviate the impact of MSUs on global decision making, which enhances the achievable throughput of an honest secondary user.

An Efficient Location Privacy-Preserving Authentication Scheme for Cooperative Spectrum Sensing

In cognitive radio networks, cooperative spectrum sensing can improve the performance of spectrum utilization and accuracy of spectrum result. However, it suffers from security and privacy threats. Firstly, secondary users' sensing reports that have to be sent to the fusion center are heavily correlated to their locations, thereby leading to the leakage of locations of secondary users in the process of reports transmission and aggregation. Moreover, malicious secondary users are likely to submit fake sensing reports or alter sensing reports of other secondary users, which finally result in a wrong aggregated result. To address these questions, we propose an efficient location privacy-preserving authentication scheme for cooperative spectrum sensing. To be specific, our scheme incorporates a reputation mechanism and enables reliable secondary users to participate in cooperative spectrum sensing. Selected users with pseudo IDs instead of real identities send encrypted sensing reports to the fusion center. This can eliminate the correlation between sensing reports and secondary users and prevent the fusion center from associating sensing reports with secondary users' real identities while decrypting and aggregating reports, thereby protecting the location privacy of secondary users. Besides, to ensure a true aggregated result, we utilize elliptic curve cryptography technique to verify the legitimacy of sensing reports when the fusion center receives them. Theoretical analyses show that the proposed scheme protects the location privacy of secondary users. Numerical results demonstrate that the proposed scheme brings less overhead than previous schemes.

Combating PUE Attack in Cognitive Radio Networks using RSSI Based EKF and UKF

The problem of spectrum scarcity in wireless communication can be reduced by Cognitive Radio (CR) technology in which the spectrum holes or unused spectrum can be allocated to the secondary or unlicensed users. But the major problem in CR is providing security. Various security issues are present at different layer. One of the major wide spread security issue in the cognitive radio is the Primary user emulsion attack in which the malicious secondary user emulated as primary user to get spectrum resources for a long time. One of way to avoided PUEA is, by obtaining the location of malicious user. The conventional location detection techniques such as time of arrival, time difference of arrival and direction of arrival, will give better performance when the user is stationary. Even though Received Signal Strength Indicator along RF fingerprint technique gives the better location of mobile user but it requires the more hardware. Hence the cost is high. In this paper, the author proposed an algorithm to locate the attacker using EKF and UKF with RSSI. In this algorithm, the initial position of user can be obtained by Received Signal Strength Indicator. This initial position integrated to EKF and UKF to track the location of primary user, which is a mobile user so that PUE Attack can be identified and avoided. The author also compares the performance of Extended Kalman Filter with Unscented Kalman Filter by Matlab software.

Lightweight Privacy Preservation for Securing Large-Scale Database-Driven Cognitive Radio Networks with Location Verification

The database-driven cognitive radio networks (CRNs) are regarded as a promising approach to utilizing limited spectrum resources in large-scale Internet of Things (IoT). However, database-driven CRNs face some security and privacy threats. Firstly, secondary users (SUs) should send identity and location information to the database (DB) to obtain a list of available channels, such that the curious DB might easily misuse and threaten the privacy of SUs. Secondly, malicious SUs might send fake location information to the DB in order to occupy channels with better quantity in advance and so gain benefits. This might also cause serious interference to primary users (PUs). In this paper, we propose a lightweight privacy-preserving location verification protocol to protect the identity and location privacy of each SU and to verify the location of SUs. In the proposed protocol, the SU does not need to provide location information to request an available channel from the DB. Therefore, the DB cannot get the location information of any SU. In the proposed protocol, the base station (BS) selects some SUs as witnesses to generate location proofs for each other in a distributed fashion. This new witness selection mechanism makes the proposed protocol reliable when a malicious SU generates fake location information to cheat the BS and also prevents SU-Witness collusion attacks. The results also show that the proposed protocol can provide strong privacy preservation for SUs and can effectively verify the location of the SUs. The security analysis shows that the proposed protocol can resist various types of attacks. Moreover, compared with previous protocols, the proposed protocol is lightweight because it relies on symmetric cryptography and it is unaffected by the area covered by the DB.

A Reinforcement Learning-Based Trust Model for Cluster Size Adjustment Scheme in Distributed Cognitive Radio Networks

Cognitive radio enables secondary users (SUs) to explore and exploit the underutilized licensed channels (or white spaces) owned by the primary users. To improve the network scalability, the SUs are organized into clusters. This article proposes a novel artificial intelligence-based trust model approach that uses reinforcement learning (RL) to improve traditional budget-based cluster size adjustment schemes. The RL-based trust model enables the clusterhead to observe and learn about the behaviors of its SU member nodes, and revoke the membership of malicious SUs in order to ameliorate the effects of intelligent and collaborative attacks, while adjusting the cluster size dynamically according to the availability of white spaces. The malicious SUs launch attacks on clusterheads causing the cluster size to become inappropriately sized while learning to remain undetected. In any attack and defense scenario, both the attackers and the clusterhead adopt RL approaches. Simulation results have shown that the single-agent RL (SARL) attackers have caused the cluster size to reduce significantly; while the SARL clusterhead has slightly helped increase its cluster size, and this motivates a rule-based approach to efficiently counterattack. Multi-agent RL attacks have shown to be less effective in an operating environment that is dynamic.

A Security-Enhanced Cluster Size Adjustment Scheme for Cognitive Radio Networks

Cognitive radio network (CRN) is the next generation wireless network that allows unlicensed users [secondary users (SUs)] to explore and use the underutilised licensed channels (white spaces) owned by licensed users (primary users). The purpose is to increase the spectrum utilization for enhanced network performance. Clustering segregates SUs in a CRN into logical groups (clusters) with each consisting of a leader (cluster head) and member nodes. A budget-based cluster size adjustment scheme is applied to enable each cluster to adjust its number of member nodes in its cluster based on the availability of white spaces in order to improve network scalability. However, cluster size adjustment is prone to attacks by malicious SUs that launch random and intelligent attacks. Hence, we incorporate an artificial intelligence approach called reinforcement learning (RL) into a trust model to countermeasure the random and intelligent attacks. The simulation results show that RL-based trust model increases the utilization of white spaces and cluster size to improve network scalability and enhance network performance despite the presence of RL-based intelligent attacks.

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.

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.

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.

Learning for Robust Routing Based on Stochastic Game in Cognitive Radio Networks

This paper studies the problem of robust spectrum-aware routing in a multi-hop, multi-channel Cognitive Radio Network (CRN) with the presence of malicious nodes in the secondary network. The proposed routing scheme models the interaction among the Secondary Users (SUs) as a stochastic game. By allowing the backward propagation of the path utility information from the next-hop nodes, the stochastic routing game is decomposed into a series of stage games. The best-response policies are learned through the process of smooth fictitious play, which is guaranteed to converge without flooding of the information about the local utilities and behaviors. To address the problem of mixed insider attacks with both routing-toward-primary and sink-hole attacks, the trustworthiness of the neighbor nodes is evaluated through a multi-arm bandit process for each SU. The simulation results show that the proposed routing algorithm is able to enforce the cooperation of the malicious SUs and reduce the negative impact of the attacks on the routing selection process.

Primary User Boundary Detection in Cognitive Radio Networks: Estimated Secondary User Locations and Impact of Malicious Secondary Users

Cognitive radio is an effective technology to improve spectrum utilization through spectrum sharing among primary users (PUs) and secondary users (SUs). Developing accurate and efficient methods to determine the PU coverage is crucial in order to avoid potential interference from SU to PU. In this paper, we propose to use the estimated location of each SU, which is obtained using the self-organizing maps algorithm, to determine the boundary of PU coverage. Furthermore, the PU boundary detection issue considering the presence of malicious SUs is also investigated. Support vector machine (SVM) algorithm, considering both linear and kernel cases, is implemented as the PU boundary detection method. Numerical results show that the boundary detection accuracy based on the estimated network layout approaches the results obtained from an accurate network layout (i.e., each SU with precise location information) and the kernel-based SVM classification results outperform the linear SVM method. In addition, PU boundary detection performance in the presence of malicious SUs is evaluated, in which three different $K$ nearest neighbor based malicious SU detection algorithms are utilized.

Robust Cooperative Spectrum Sensing Based on Adaptive Reputation and Evidential Reasoning Theory in Cognitive Radio Network

In this paper we investigate the problem of cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs) in the presence of malicious attacks from secondary users (SUs). We propose a novel CSS approach based on a new adaptive reputation and evidential reasoning theory (ERT) to detect the malicious SUs, to strengthen the sensing ability, and to keep good stability and generalization ability. Different from the previous work, we assume that the fusion center has no prior information of the radios in CRNs except the condition that the honest SUs are in majority. Specifically, to counter different attack strategies, we present an adaptive reputation which fully considers the signal-to-noise ratio, historical sensing data information, and current local sensing results via an energy detection method. The proposed adaptive reputation can effectively identify the malicious SUs when the CRNs are with single-attack or multiple-attack models. Furthermore, we present a CSS scheme via the proposed adaptive reputation and ERT on the basis of local sensing results derived from the energy detection method. The new CSS scheme has good robustness and generality and can decrease the computational complexity and overcome the evidence conflict problems of Dempster–Shafer theory-based CSS schemes. Extensive simulations are carried out to demonstrate the performance and the superiority of the proposed approach in contrast with previous methods.