Attacks In Cognitive Radio Networks Research Articles

Defending dominant cooperative probabilistic attack in CRNs by JS-divergence-based improved reputation algorithm

Rapid advances in wireless communication services has made limited spectrum resources increasingly scarce. One promising solution for enhancing spectrum utilization is cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs). However CSS is vulnerable to Byzantine attack. Current researches show that Byzantine attack is easily defended for their fixed attack probability. In this context, we propose an improved attack model called the dominated cooperative probabilistic attack (DCPA) model in the actual situation, building upon the generalized collaborative probabilistic Byzantine attack model. This DCPA model contains auxiliary cooperative attackers (ACAs) who launch attacks and a dominant attacker (DA) who determines ACAs’ attack probability intervals based on their respective credibility. The DCPA model allows ACAs to flexibly launch attacks, without being identified by the traditional reputation defense algorithm, significantly compromising the sensing performance of CSS. To successfully resist the threat posed by the DCPA model to CSS, we propose a JS-divergence-based improved reputation algorithm that can distinguish honest users (HUs) from attackers. This algorithm analyzes two consecutive sensing reports to identify differences in sensing behavior between HUs and attackers. Through Python simulation analysis, we demonstrate that, compared to the generalized cooperative probabilistic attack (CPA) model and the attack-free CSS (AFC) model, the proposed DCPA model is more concealed and significantly more disruptive to the performance of traditional reputation defense algorithms. Furthermore, our approach greatly enhances the performance of CSS by promoting the participation of HUs and suppressing attackers during the final data fusion. And also compared with the PAM2 algorithm, the conventional voting rule (CVR) algorithm and the traditional reputation defense algorithm, our proposed algorithm improves the detection performance by at least 7%, 15% and 50%.

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.

A Novel Self-Exploration Scheme for Learning Optimal Policies against Dynamic Jamming Attacks in Cognitive Radio Networks

Abstract Cognitive Radio Networks (CRNs) present a compelling possibility to enable secondary users to take advantage of unused frequency bands in constrained spectrum resources. However, the network is vulnerable to a wide range of jamming attacks, which adversely affect its performance. Several countermeasures proposed in the literature require prior knowledge of the communication network and jamming strategy that are computationally intensive. These solutions may not be suitable for many real-world critical applications of the Internet of Things (IoT). Therefore, a novel self-exploration approach based on deep reinforcement learning is proposed to learn an optimal policy against dynamic attacks in CRN-based IoT applications. This method reduces computational complexity, without prior knowledge of the communication network or jamming strategy. A simulation of the proposed scheme eliminates interference effectively, consumes less power, and has a better Signal-to-Noise Ratio (SNR) than other algorithms. A platform-agnostic and efficient anti-jamming solution is provided to improve CRN’s performance when jamming occurs.

Massive SSDF Attackers Identification in Cognitive Radio Networks by Using Consistent Property

To resist probabilistic spectrum sensing data falsification (SSDF) attacks in cognitive radio networks (CRNs), we in this paper propose a simple and effective attacker identification scheme from the perspective of each secondary user's (SU) historical sensing data without any prior knowledge about the strategy of attackers. In the proposed algorithm, the inconsistency property of the historical data within two consecutive sensing slots during a sensing time window is extracted to characterize different attack behaviors. Further, an optimal identification threshold is obtained for each SU and the analytical expressions of identification performance are also derived. Finally, simulation results along with theoretical analysis show the validity of the proposed scheme to defend against massive probabilistic SSDF attacks with low computation cost.

Sequential spectrum sensing against random Byzantine attack in cognitive radio networks

AbstractCognitive radio (CR) technology allows secondary users (SUs) to opportunistically access the licensed spectrum being authorized by the primary users (PUs) and then improves the spectrum utilization. Cooperative spectrum sensing (CSS) is an important component of cognitive radio (CR) technology to detect the PU signal via spatial diversity. However, due to the open facet of CR networks (CRNs), the CSS process may suffer from Byzantine attack where malicious users (MUs) falsify the status information about the PU signal and then cause harmful interference to the PU's communication or selfishly occupy spectrum resources. Otherwise, the cooperative paradigm also results in that multiple SUs need more time to detect the PU signal. The performance and efficiency of CSS will be further degraded due to these two reasons. For this aim, we propose sequential spectrum sensing (SSS) scheme to mitigate the negative impact of Byzantine attack and improve the cooperative efficiency. To this end, encouraged by in‐depth analysis of the blind problem, we formulate a random Byzantine attack model to follow the malicious behaviors from the perspective of MUs. In order to solve the blind problem in the process of the sensing sample fusion, a delivery evaluation mechanism is developed to lay solid foundation for SSS. On this basis, we further evaluate the reputation value (RV) within a sliding window and in order to improve the cooperative efficiency in the reporting stage, we dynamically select the sensing results of the more reliable SUs as the global decision to make a tradeoff between cooperative performance and cooperative efficiency. At last, extensive simulation results show that regardless of the malicious ratio, our proposed SSS only requires 6 samples to provide with 100% detection accuracy in the presence of always attack while also the remarkable performance in the presence of random attack when the malicious ratio exceeds 50%.

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.

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.

Comprehensive Performance Analysis of Soft Data Fusion Schemes under SSDF Attacks in Cognitive Radio Networks

Background: Trust and security are the biggest challenges facing the Cooperative Spectrum Sensing (CSS) process in Cognitive Radio Networks (CRNs). The Spectrum Sensing Data Falsification (SSDF) attack is considered the biggest threat menacing CSS. Methods: This paper investigates the performance of different soft data combining rules such as Maximal Ratio Combining (MRC), Square Law Selection (SLS), Square Law Combining (SLC), and Selection Combining (SC), in the presence of Always Yes and Always No Malicious User (AYMU and ANMU). Results: This comparative study aims to assess the impact of such malicious users on the reliability of various soft data fusion schemes in terms of miss detection and false alarm probabilities. Furthermore, computer simulations are performed to show that the soft data fusion scheme using MRC is the best in the field of soft data computing. Conclusion: ANMU has a slight impact on CSS. Yet, AYMU affects the cooperative detection performance.

Comparing Adversary Defense Mechanisms in Cognitive Radio Networks

BACKGROUND: In a cognitive radio network, the cognitive transmitter senses the medium to detect spectrum opportunities and transmits its own data if the channel is sensed to be idle. A jammer can also sense the medium and identify the slots of successful transmission. The jammer’s main objective is to reduce the throughput of the cognitive transmitter. METHODS: Towards this objective, the jammer builds a deep learning classifier in which the most recent sensing results of acknowledgments (ACKs) sent by the receiver are used to predict the slots of successful transmissions of the cognitive transmitter. This allows the attacker to reliably predict the successful transmissions and can effectively jam these transmissions. The deep learning classification soft decision probabilities are used by the jammer for power control subject to a certain power budget. A receiver-based defense mechanism is developed against the jamming attacks. The receiver purposely takes some wrong actions, i.e., sends ACK when transmission is not successful and vice versa, to poison the training process of the attacker. Results: We show that our receiver’s defense mechanism effectively enhances the throughput of the cognitive transmitter when compared to the transmitter’s defense mechanism, where the transmitter takes some wrong decisions when it accesses the medium. CONCLUSION: A novel defense mechanism against jamming attacks in cognitive radio networks is introduced.

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.

An Authentication Based Approach for Prevention of Spectrum Sensing Data Falsification Attacks in Cognitive Radio Network

This is the era of Intelligent Cognitive Radio Network (CRN) technology that provides the available spectrum with efficient utilization. Cognitive Radio (CR) technology must promise to allow interference-free spectrum access by users. The paper discusses the several attacks and motives of attacks. The authentication mechanism role to prevent the attacks for hassle-free spectrum utilization is demonstrated. In this paper, resolving the cognitive network security issues by the authentication mechanism and the methods and need of authentication is discussed. This paper addresses the research challenges in the way of securing the CRN and countermeasures in CRN security strategies. Spectrum sensing data falsification (SSDF) attack is most disruptive in which the malicious users degrade the decision-making process by sending the false sensing reports to data fusion center’s thus preventing honest users from utilizing the spectrum. In this paper, to counter the SSDF attack, the trust-based security mechanism is demonstrated to authenticate the honest users and it is observed that the proposed framework in the MATLAB environment is efficient and able to detect malicious users. CR technology is the strategy applied to the spectrum to make it efficient for wireless communication. The strategy is an intelligent way to access the spectrum as it can learn its environment and make decisions by easy adaptation of operating parameters. The initial step is to sense the spectrum available further steps are spectrum decision making, spectrum management, and spectrum mobility. The network is vulnerable to various attacks on spectrum sensing and policy protocols which lead to disturbing functionality of CR technology. The defence mechanism based on public-key cryptography is proposed in which the Primary User (PU) is authenticated by appending signature provided to PU signal. Authentication with a tag to the primary users is another perspective proposed. CRN technology should provide integrity, confidentiality and authenticity to the users.

Detecting Byzantine attacks in Cognitive Radio Networks : A two-layered approach using Hidden Markov Model and machine learning

Cognitive Radio Networks (CRN) are gaining popularity due to their ability to harness the TV whitespace. CRNs are expected to usher in a new wireless technology to cater to the ever growing population of wireless mobile devices while the current ISM range of wireless technologies is increasingly becoming insufficient. CRNs uses the principle of collaborative spectrum sensing (CSS) where unlicensed users, called Secondary Users (SU) keep sensing a licensed band belonging to the incumbent user called the Primary User (PU). Such a collaborative sensing mechanism enables an increased detection accuracy of the incumbent’s presence (or absence) as all the SUs send their local sensing reports to a Fusion Centre (FC), where these sensing reports are aggregated to arrive at a final sensing decision. However, this collaborative sensing introduces vulnerabilities which can be used to carry out an attack called the Byzantine Attack (a.k.a. Spectrum Sensing Data Falsification (SSDF) attack). We present a two-layer model framework to classify Byzantine attackers in a CRN. The first layer, Processing layer, uses Hidden Markov Model (HMM) to get a probabilistic relationship between the PU’s states and the SUs’ sensing reports. This generates the required dataset for the next layer. The second layer, Decision layer, uses several ML algorithms to classify the SUs into Byzantine attackers and normal SUs. Extensive simulation results confirm that the learning classifiers perform well across various testing parameters. Finally, a comparison analysis of the proposed method with an existing non-ML technique shows that the ML approach is more robust especially under high presence of malicious users.

Mitigation of Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks using Trust Based Cooperative Sensing

One of most emerging technology in the recent years in the field of wireless communication is the Cognitive Radio (CR) technology, which reduces spectrum scarcity significantly. The main function of CR technology is detecting spectrum holes or unused spectrum of primary users (PUs), also called as licensed users, and assigning this unused spectrum to the secondary users (SUs), also called unlicensed users. As the CR technology is open to every user, there are many security issues such as Primary User Emulsion Attack (PUEA), Jamming Attack, Spectrum Sensing Data Falsification (SSDF) Attack, Lion Attack, and Sink Hole Attack and so on. SSDF attack is the one of major security attack in cognitive radio in which a malicious user sends false data intentionally to the other secondary users. The main aim of the SSDF attack is to disturb the communication between the secondary users or to gain more channel resources. One of the solutions to mitigating SSDF attack is the cooperative spectrum sensing. In this paper, we propose a new algorithm of cooperative sensing based on trust values of secondary users, and compares with the conventional cooperative spectrum sensing with the proposed algorithm. The simulation of cooperative sensing also performed in both time variant channel and time invariant (Rayleigh) channel. The authors also compare the three basic hard fusion techniques such as AND, OR, MAJORITY rule

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

Mitigation Techniques for Puea In Cognitive Radio Networks Using Hypothesis Testing

This paper detects primary user emulation attacks in cognitive radio networks using Neyman-Pearson composite hypothesis test and Wald’s sequential probability ratio test. Most approaches in the literature on Primary user emulation attacks assume the presence of underlying sensor networks for localization of the malicious nodes. There are no analytical studies available in the literature to study Primary user emulation attacks in the presence of multiple malicious users in fading wireless environments. An NPCHT and WSPRT based analysis to detect Primary user emulation attack in fading wireless channels in the presence of multiple randomly located malicious users. It shows there is a range of network radii in which PUEA is most successful. The results also show that for the same desired threshold on the probability of missing the primary, WSPRT can achieve a successful probability PUEA 50% less than that obtained by NPCHT.

Investigations on detection and prevention of primary user emulation attack in cognitive radio networks using extreme machine learning algorithm

Cognitive radio is a competent technology which resolves the spectrum scarcity crisis in a network by allocating free spectrum dynamically and establishes coexistence between the users of the network, without interfering with the incumbent transmission. Primary user emulation attack (PUEA) is one of the major threats to the spectrum sensing performance that decreases the spectrum access probability of the unlicensed users of the network. In this paper Detection and prevention of PUEA is realized proficiently using Time-Distance with signal Strength Evaluation (TDSE) and Extreme Machine Learning (EML) algorithm. TDSE implementation makes it feasible to identify the malicious PUE attacker under mobile user stipulation and EML algorithm provides a swift comparative decision regarding the malicious attack. The technique is also proven to improve the sensing ability, energy efficiency and brings down the overall delay of the system, illustrating a considerable improvement in the overall network performance and spectrum utilization in a cognitive radio network.

AdS: An adaptive spectrum sensing technique for survivability under jamming attack in Cognitive Radio Networks

IEEE 802.22 Cognitive Radio Network (CRN) allows sharing of geographically unused spectrum allocated to the television broadcast service, on a non-interfering basis. CRNs make use of Dynamic Spectrum Access (DSA) which is a two-stage mechanism comprising fast sensing, which is mandatory and fine sensing which is optional. However, the two-stage spectrum sensing mechanism suits malicious nodes in the CRN which intend to deny the use of vacant spectrum to the CRN by jamming its communication while at the same time, minimizing the amount of power expended on jamming. Such Minimal Denial of Service (MDoS) jamming attack can be launched by the malicious nodes by transmitting a very short jamming signal during the mandatory fast sensing stage which will in turn force the CRN to carry out the otherwise optional, fine sensing. MDoS jamming attack results in wastage of spectrum opportunities for the rest of the CRN to the extent which can jeopardize its survivability. In this paper we present AdS: an Intelligent Adaptive spectrum Sensing technique which, not only minimizes the effects of MDoS jamming attack but can also reduce the effects of noise during the spectrum sensing stages of DSA. The adaptive nature of AdS improves spectrum utilization by CRNs by up to 90% through adaptive tuning of the fine sensing threshold which is based on an estimate of PU’s activity and the severity of MDoS jamming attack.

The Defense Against Jamming Attack in Cognitive Radio Networks: Energy Efficiency Management Perspective

Cognitive Radio Networks (CRN) learning perspective and inherently reconfigurable capability generate a group of novel safety problems. Here, let's analyze the jamming security issue with a smart jammer, which could rapidly study the broadcast power of the users also flexibly regulate their broadcast power towards maximizing the destructive effects. Additionally, the Secondary User (SU) spends energy while switching to next frequency channel, cause degradation of the system performances with respect to energy effectiveness. Here, we explore the use of Stackelberg game to achieve jamming resilient radio transmission and power control strategy of an SU. The optimal power allocation is carried out with the iterative water-filling algorithm (WFA) and Channel Allocation based on Stackelberg Game algorithm (CASG) exhibits the Stackelberg equilibrium and the min-max/max-min best power allocation over the recreation of the various game situations.

An Energy-Efficient Jamming Attacks Detection based on Cognitive Radio Networks

5G wireless networks, primarily because of the device - to - device connections can enable huge networking. Dynamic bandwidth connectivity is a feature that enables device-to-device connections. Applications configured with cognitive radios must be authorized to reprocess the bandwidth consumed by cellular connections. The complex efficiency of the bandwidth allows cognitive consumers to switch between networks. In specific, switching contributes to energy efficiency, delay, and bandwidth connectivity. When the system is under the jamming attack, the computational cost much more. It is a major problem to fix jamming while ensuring an optimal level of operation. Thus, existing anti-jamming methods consider static users, this suggests preventive measures for wireless cognitive radio users in this paper and test them. The multivariate cumulative total was used in this study to classify suspicious activity like jamming attacks in Cognitive Radio Networks (CRN). Preventive-measures are being taken to resolve security risks to cognitive radio networks. The Intrusion Detection System (IDS) has been presented, including a way of preventing attacks on the cognitive radio network infrastructure.

Rollout algorithm for light‐weight physical‐layer authentication in cognitive radio networks

Cognitive radio networks (CRNs) are vulnerable to spoofing attacks due to their wireless and cognitive nature. Since the traditional cryptographic authentication can hardly prevent such attacks in CRNs, the physical-layer authentication has been investigated for recent years. To achieve a light-weight physical-layer authentication, a rollout partially observable Markov decision process-based algorithm, named RoPOMDP, is proposed in this study. In general, RoPOMDP formulates the physical-layer authentication as a zero-sum game, based on which a hypothesis test upon channel vectors is developed. That allows us to design the gains for both spoofers and receivers based on Bayesian risks for the game, in which the spoofing attack probability is predicted by a non-linear function approximation utilising v-support vector regression. Then, a RoPOMDP is employed to estimate the optimal threshold for the test statistic such that spoofing attacks can be detected. The theoretical analysis and simulations indicate that: (i) RoPOMDP improves the spoofing detection accuracy; (ii) as a light-weight algorithm, the complexity of RoPOMDP is lower than contemporary ones.