Primary User Emulation Attack Research Articles

Discrimination of primary user emulation attack on cognitive radio networks using machine learning based spectrum sensing scheme

Discrimination of primary user emulation attack on cognitive radio networks using machine learning based spectrum sensing scheme

A Reliable Spectrum Sensing Method Based on Deep Learning for Primary User Emulation Attack Detection in Cognitive Radio Network

A Reliable Spectrum Sensing Method Based on Deep Learning for Primary User Emulation Attack Detection in Cognitive Radio Network

Enhanced PUE Detection and Selfish Secondary User Detection Method in Cognitive Radio Networks

In cognitive radio networks (CRNs),cognitive radio (CR) nodes adaptively access the spectrum aiming to maximize the utilization of the scarce resource. A new security threat known as the Primary User Emulation attack raises a great challenge to CRNs. In the proposed method an innovative technique is introduced which is called as Enhanced PUE detection method and selfish secondary user detection method. In the Enhanced PUE detection, PUE detection approach can be extended to address the scenario with multiple classes of PUs that have different SAP features. The Selfish Secondary User detection method, attacks are detected by the cooperation of other legitimate neighboring Secondary Users.

Detection of Emulation Attacks in Cognitive Radio Networks using Heuristic Techniques

This paper discusses the issue of primary user emulation attacks (PUEAs) in cognitive radio networks (CRNs). These attackers imitate the signal characteristics of primary users (PUs), preventing secondary users (SUs) from accessing their assigned spectrum. The research focuses on detecting PUEAs using the time difference of arrivals (TDOA) to detect the attacker and reduce detection errors using heuristic techniques. Differential evolution (DE) and cuckoo search (CS) algorithms are utilized to optimize detection accuracy. Simulation results confirm that the proposed CS algorithm outperforms and gives better performance as compared to the DE algorithm.

Defense Management Mechanism for Primary User Emulation Attack Based on Evolutionary Game in Energy Harvesting Cognitive Industrial Internet of Things

Defense Management Mechanism for Primary User Emulation Attack Based on Evolutionary Game in Energy Harvesting Cognitive Industrial Internet of Things

PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks

The primary user emulation (PUE) attack is one of the strongest attacks in mobile cognitive radio networks (MCRN) because the primary users (PU) and secondary users (SU) are unable to communicate if a malicious user (MU) is present. In the literature, some techniques are used to detect the attack. However, those techniques do not explore the cooperative detection of PUE attacks using deep neural networks (DNN) in one MCRN network and with experimental results on software-defined radio (SDR). In this paper, we design and implement a PUE attack in an MCRN, including a countermeasure based on the entropy of the signals, DNN, and cooperative spectrum sensing (CSS) to detect the attacks. A blacklist is included in the fusion center (FC) to record the data of the MU. The scenarios are simulated and implemented on the SDR testbed. Results show that this solution increases the probability of detection (PD) by 20% for lower signal noise ratio (SNR) values, allowing the detection of the PUE attack and recording the data for future reference by the attacker, sharing the data for all the SU.

To mitigate primary user emulation attack trajectory using cognitive single carrier frequency division multiple access approaches: Towards next generation green IoT

AbstractThe growing cognizance of spectrum scarcity has become a more significant concern in wireless radio communications. Due to the exponential growth of data transmission in intelligent wireless sensor networks, energy spectrum detection has become a promising solution for resolving spectrum shortages. Primary user emulation attack (PUEA) has been identified as a significant attack vector in the cognitive radio (CR) domain's physical layer. In comparison, the CR is a promising method to increase spectrum efficiency by allowing unlicensed secondary users (SUs) to access licensed frequency bands without interfering with approved primary users (PUs). The study's primary findings are the methodology for preventing PUEA using authentication tags, which are unique sequences. This research blends SC‐FDMA with CR to protect CR networks from PUEA attacks, a Latin square (LS) matrix tag generation system is proposed to mitigate the PUEA effect. The technology is meant to provide effective authentication and protection against malicious users. In a secured environment, the LS tag technique is utilized to track and estimate the PU. For example, the BER of both techniques is virtually identical between 0 and 4 dB, while the BER performance of the suggested LS tag generation improves with increasing signal‐to‐noise ratio (SNR). As a result, the suggested LS tag generation is less susceptible to PUEA. To diminish the influence of PUEA in CR networks, an efficient enlightening approach for making the future Green Cognitive Radio Wireless networks structure is proposed. The simulation results also demonstrate the resilience of the proposed CR spectrum sensing techniques for energy‐efficient knowledge at varying degrees to reduce the adverse effects of environmental technologies.

Hermitan matrices based malicious cognitive radio detection and bayesian method for detecting primary user emulation attack

Cognitive radio (CR) is a facilitating technology to efficiently deal with the spectrum scarceness, and it will significantly enhance the spectrum deployment of upcoming wireless transmission method. Security is a significant concern, although not well tackle in cognitive radio networks (CRN). In CR networks, this approach regard as a security issue happen from primary user emulation attack (PUEA). A PUEA attacker forwards an emulated primary signal and defraud the CR users to avoid them from accessing spectrum holes. Here, we introduce a Hermitan matrices based malicious cognitive radio (CMCR) detection and Bayesian method for detecting PUEA attack in the CRN. In this approach, the Bayesian method is used for detecting the PUEA attack. The trust analyzer evaluates the CR trust. Here, the node trust value is computed by node activeness and inactiveness, degree of data transmission, and hermitan matrics verification. In addition, the Hermitan Matrices method is used to detect the malicious CR user in the CRN. The simulation outcomes propose that the CMCR leads to improve the performance in terms of better detection ratio, minimized the possibility of miss detection ratio. Furthermore, it minimized the possibility of false alarm in the CRN.

Energy-efficient adaptive sensing for Cognitive Radio Sensor Network in the presence of Primary User Emulation Attack

A Cognitive Radio Sensor Network (CRSN) is a promising technology incorporating cognitive radio in traditional wireless sensor networks. In general, Primary User Emulation Attack (PUEA) is becoming the major bottleneck for implementing CRSN in reality. In CRSN-PUEA, malicious users are also equipped with a Spectrum Sensing capability, the so-called smart PEUA, which further complicates the spectrum access of Cognitive Users (CU). In this paper, we propose a novel Energy-Efficient Adaptive Sensing (EEAS) technique for CRSN to overcome the spectrum access problem in the presence of PUEA. The proposed EEAS enhances the sensing accuracy by differentiating the PUEA and Primary User signals that, reduce the sensing error probability and enhance the underlying system model’s throughput and energy efficiency. The numerical results show that the proposed EEAS technique has improved the throughput and energy efficiency by about 12% and 44%, respectively, compared to the state-of-the-art works in the field of PUEA-CRSN. In addition, it is observed that the sensing error probability is reduced by 22%. Furthermore, a Software Defined Radio (SDR) based hardware system setup has been developed to verify the functionality of the proposed system model.

Detection of Primary User Emulation Attack Using the Differential Evolution Algorithm in Cognitive Radio Networks

Cognitive Radio Network (CRN) is an emerging technology used to solve spectrum shortage problems in wireless communications. In CRN, unlicensed secondary users (SUs) and licensed primary users (PUs) use spectrum resources at the same time by avoiding any interference from SUs. However, the spectrum sensing process in CRN is often disturbed by a security issue known as the Primary User Emulation Attack (PUEA). PUEA is one of the main security issues that disrupt the whole activity of CRN. The attacker transmits false information to interrupt the spectrum sensing process of CRN, which leads to poor usage of the spectrum. The proposed study uses a proficient Time Difference of Arrival (TDOA) based localization method using the Differential Evolution (DE) algorithm to identify the PUEA in CRNs. The DE algorithm is used to solve the objective function of TDOA values. The proposed methodology constructs a CRN and identifies PUEA. The proposed method aims to sense and localize PUEA efficiently. Mean Square Error (MSE) is the performance evaluation parameter that is used to measure the accuracy of the proposed technique. The results are compared with the previously proposed Firefly optimization algorithm (FA). It is clear from the results that DE converges faster than FA.

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.

Signal Processing-based Model for Primary User Emulation Attacks Detection in Cognitive Radio Networks

Cognitive Radio Networks (CRNs) have been conceived to improve the efficiency of accessing the spectrum. However, these networks are prone to various kinds of attacks and failures that can compromise the security and performance of their users. One of the notable malicious attacks in cognitive radio networks is the Primary User Emulation (PUE) attack, which results in underutilization and unavailability of the spectrum and low operational efficiency of the network. This study developed an improved technique for detecting PUE attacks in cognitive radio networks and further addressed the characteristics of sparsely populated cognitive radio networks and the mobility of the primary users. A hybrid signal processing-based model was developed using the free space path loss and additive Gaussian noise models. The free space path loss model was used to detect the position of the transmitter, while the additive Gaussian noise model was used to analyze the signal transmitted, i.e., energy detection in the spectrum at the detected location. The proposed model was benchmarked with an existing model using the number of secondary users and the velocity of the transmitter as performance parameters. The simulation results show that the proposed model has improved accuracy in detecting primary user emulation attacks. It was concluded that the proposed hybrid model with respect to the number of secondary users and the velocity of the transmitter can be used for primary user emulation attack detection in cognitive radio networks.

Mitigation of primary user emulation attack using a new energy detection method in cognitive radio networks

Mitigation of primary user emulation attack using a new energy detection method in cognitive radio networks

Enhanced Primary User Emulation Attack Inference in Cognitive Radio Networks Using Machine Learning Algorithm

Cognitive Radio (CR) is a competent technique devised to smart sense its surroundings and address the spectrum scarcity issues in wireless communication networks. The Primary User Emulation Attack (PUEA) is one of the most serious security threats affecting the performance of CR networks. In this paper, machine learning (ML) principles have been applied to detect PUEA with superior decision-making ability. To distinguish the attacking nodes, Reinforced Learning (RL) and Extreme Machine Learning (EML-RL) algorithms are proposed to be based on Reinforced Learning (EML). Various dynamic parameters like estimation error, attack detection efficiency, attack estimation rate, and learning rate have been examined with the Network Simulator 2 (NS2) tool.

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

The exponential growth of Internet connected systems has generated numerous challenges, such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions. Complicated and dynamic SS systems can be exposed to different potential security and privacy issues, requiring protection mechanisms to be adaptive, reliable, and scalable. Machine learning (ML) based methods have frequently been proposed to address those issues. In this article, we provide a comprehensive survey of the recent development of ML based SS methods, the most critical security issues, and corresponding defense mechanisms. In particular, we elaborate the state-of-the-art methodologies for improving the performance of SS communication systems for various vital aspects, including ML based cognitive radio networks (CRNs), ML based database assisted SS networks, ML based LTE-U networks, ML based ambient backscatter networks, and other ML based SS solutions. We also present security issues from the physical layer and corresponding defending strategies based on ML algorithms, including Primary User Emulation (PUE) attacks, Spectrum Sensing Data Falsification (SSDF) attacks, jamming attacks, eavesdropping attacks, and privacy issues. Finally, extensive discussions on open challenges for ML based SS are also given. This comprehensive review is intended to provide the foundation for and facilitate future studies on exploring the potential of emerging ML for coping with increasingly complex SS and their security problems.

Online Primary User Emulation Attacks in Cognitive Radio Networks Using Thompson Sampling

Spectrum sensing is one of the main components of the cognitive radio (CR) system, based on which secondary users (SUs) can get access to spectrum holes available. On the other hand, a malicious adversary can also attack the primary user (PU) system and the legitimate CR system via spectrum sensing, which can lead to serious security issue for both systems. In this article, we study an online attacking strategy, referred to as PU emulation attacker (PEA), which transmits forged PU signals over available channels to deteriorate the spectrum sensing performance of the SUs. We propose an online learning based attacking scheme for both the single attacker and the multiple-attacker cases, and analyze the regret upper bound of the proposed algorithm. The proposed PEA strategy can work in both stationary and non-stationary CR networks where the statistical characteristics of channels and the access strategy of SUs change over time. Numerical results show that it is more efficient than others in two different performance metrics: successful accesses of SUs and effective attacks of PEAs. Our proposal raises an interesting open question on how to develop CR networks with security guarantee.

Performance Analysis of a Cognitive Radio Network Under Energy Harvesting from Primary User Emulation Attacker

Performance Analysis of a Cognitive Radio Network Under Energy Harvesting from Primary User Emulation Attacker

Cognitive Radio Networks Based on Users' Reputation Scheme

Cognitive Radio (CR) can be defined as a technology that allows users to change the transmission parameters as required to increase the spectrum efficiency. Because of this mechanism, some threats emerge. Two major threats are found in CR. The first is the Primary User Emulation Attack (PUEA), where the attacker is able to transmit at a forbidden time slot effectively, emulating the signals of the primary user. This makes all the system users believe that the spectrum is occupied by a good primary user. The second threat is known as the spectrum sensing data falsification attack (SSDF). In this case, the attackers send false observation information, intentionally or unintentionally, to the fusion center (FC), causing it to make the wrong decision. In this study, the scheme presented was based on a users' reputation for secure spectrum access in cognitive radio networks. Each Secondary User (SU) performs local sensing and then forwards the sensing results to the main fusion center FC. The FC makes the final decision about the presence of the primary user based on the proposed approach. The schemes substantially reduce the effect of cognitive users with low reputation values while improving the impact of cognitive users with the high reputation values on the final decision. It has been verified that the proposed approach can improve the sensing performance under the impact of a different number of reliable and unreliable users in a CR network. Results based on simulation show that the proposed scheme outperforms the traditional majority scheme despite a high number of malicious users.

Analytical Model for Mitigating Primary User Emulation Attack using Hypothesis Testing in Cognitive Radio Networks

The Cognitive Radio (CR) is the key technology to deal with spectrum scarcity by allowing unlicensed CR users to coexist with existing users in licensed spectrum bands without interfering with binding communications. Cognitive technology provides the spectrum ability to be shared with licensed networks. This radio system can adjust its transmitter and recipient parameters on the basis of interaction with the current conditions in the environment. Due to this flexibility cognitive radios are exploring different types of threats and security attacks. In this paper, we focus primarily on the Primary User Emulation Attack (PUEA) which is a major attack in the cognitive radio network. PUEA is similar to a Denial-of-service attack that can seriously disrupt the spectrum sensing process and deny other legitimate secondary users to access the available spectrum. We proposed a Neyman-Pearson composite hypothesis test (NPCHT) based analytical model to study the impact of PUEA in a fading wireless communication environment. Simulation results show that the proposed techniques can substantially reduce the impact of the malicious attack on the network

Detection and Prevention of Primary User Emulation Attack in Cognitive Radio Networks Using Secure Hash Algorithm

Detection and Prevention of Primary User Emulation Attack in Cognitive Radio Networks Using Secure Hash Algorithm