Security for Cognitive Radio Networks

In cognitive radio networks, wireless nodes adapt to the surrounding radio environment and utilize the spectrum of licensed users. The cognitive radio environment is dynamic, and wireless channels are accessible by both legitimate and illegitimate users. Therefore, maintaining the security of cognitive radio networks is a challenging task, which must be addressed thoroughly. Further, with the recent exponential surge in wireless nodes and associated high data rate requirements, energy consumption is also growing at an unprecedented rate. Hence, energy efficiency becomes an important metric that must be considered in the design of future wireless networks. Accordingly, by considering the great ecological and economic benefits of green wireless networks, this work focus on energy-efficient resource allocation in secure cognitive radio networks. Since physical-layer security is an emerging technique that improves the security of communication devices, in this paper, an ergodic secure energy efficiency problem for a cognitive radio network is formulated with a primary user, a secondary user, and an eavesdropper. As the formulated problem is non-convex, a concave lower bound is applied to transform the original non-convex problem into a convex one. Further, by adopting the fractional programming and dual decomposition techniques, optimal power allocation strategies are obtained with the aim of maximizing the ergodic secure energy efficiency of the secondary user with constraints on the average interference power and average transmit power. Numerical examples are used to demonstrate the effectiveness of the proposed algorithm.

This paper discusses the topic of wireless security in cognitive radio networks, delineating the key challenges in this area. With the ever-increasing scarcity of spectrum, cognitive radios are expected to become an increasingly important part of the overall wireless networking landscape. However, there is an important technical area that has received little attention to date in the cognitive radio paradigm: wireless security. The cognitive radio paradigm introduces entirely new classes of security threats and challenges, and providing strong security may prove to be the most difficult aspect of making cognitive radio a long-term commercially-viable concept. This paper delineates the key challenges in providing security in cognitive networks, discusses the current security posture of the emerging IEEE 802.22 cognitive radio standard, and identifies potential vulnerabilities along with potential mitigation approaches.

Cognitive radio (CR) networks have attracted many attentions recently, while the security issues are not fully studied yet. In this paper, we propose a new and powerful network layer attack, routing-toward-primary-user (RPU) attack in CR networks. In this attack, malicious nodes intentionally route a large amount of packets toward the primary users (PUs), aiming to cause interference to the PUs and to increase delay in the data transmission among the secondary users. In the RPU attack, it is difficult to detect the malicious nodes since the malicious nodes may claim that those nodes, to which they forward the packets, behave dishonestly and cause problems in the data transmission. To defend against this attack without introducing high complexity, we develop a defense strategy using belief propagation. First, an initial route is found from the source to the destination. Each node keeps a table recording the feedbacks from the other nodes on the route, exchanges feedback information and computes beliefs. Finally, the source node can detect the malicious nodes based on the final belief values. Simulation results show that the proposed defense strategy against the RPU attack is effective and efficient in terms of significant reduction in the delay and interference caused by the RPU attack.

The unguided transmission nature of wireless communication channels in cognitive radio networks (CRNs) makes it easier for attackers to gather transmitted data or to avoid transmissions when compared to traditional wired systems. Furthermore, the shared spectrum concept may lead to various security threats and misusages, especially in the physical (PHY) layer with attacks such as primary user emulation and spectrum sensing data falsification. In this paper, the common PHY layer attacks are introduced from CRN perspective with the corresponding detection techniques and countermeasures. A correlation based trust factor is defined to identify attacks in CRNs, and a beamforming approach is proposed for their prevention. An optimization problem is defined to determine the beamforming coefficients that minimize the success of especially the spectrum sensing data falsification attack. The performance of the proposed method is compared with non-optimized techniques in different channel conditions and proven to be an efficient countermeasure by simulation results.

Cognitive Radio Networks (CRN) are the possible and ideal solution for meeting the spectrum needs of next-generation communication systems. CRN is a promising alternative approach that allows spectrum sharing in many applications. The licensed users considered Primary Users (PU) and unlicensed users as Secondary Users (SU). Time and power consumption on security issues are considered degrading factors in performance for improving the Quality of Service (QoS). Irrespective of using different optimization techniques, the same methodology is to be updated for the task. So that, learning and optimization go hand in hand. It ensures the security in CRN, risk factors in spectrum sharing to SU for secure communication. The objective of the proposed work is to preserve the location of the SU from attackers and attain the clustering of SU to utilize the resource. Ant Colony Optimization (ACO) is implemented to increase the overall efficiency and utilization of the CRN. ACO is used to form clusters of SUs in the co-operative spectrum sensing technique. This paper deals with threat detection and classifying threats using parameters such as unlikability, context privacy, anonymity, conditional traceability, and trade-off. In this privacy-preserving model, overall accuracy is 97.4%, and it is 9% higher than the conventional models without Privacy-Preserving Architecture (PPA).

As the proliferation of wireless handheld devices such as smart phones and tablets grow exponentially, new technologies have to be adopted to efficiently use the radio frequency spectrum. Cognitive Radio(CR) network offers a promising solution to improve spectrum utilization and efficiency by harnessing the available spectrum resources in real-time. However, participants in the CR network can comprise of malicious adversaries who adopt dishonest and non-cooperative strategies to attack the network. In our research, we focus on a critical attack on the CR network: Primary User Emulation (PUE). In a PUE attack, the malicious adversary violates spectrum sharing etiquette by impersonating the primary user at the expense of other unselfish secondary users. To thwart such an attack, we present a strategy to authenticate the primary user, which involves collaborative learning among CR users.

Malicious user is internal attacker that disturb communication. In case of cognitive radio, malicious user attacks primary signal detection and influences the fidelity of sensing outcome. This paper studies malicious user attack and its impacts to sensing outcome in cognitive radio network (CRN). We consider a number of malicious user that emulate primary user (PU) signal. Malicious users are randomly deployed around secondary user (SU) at a certain distance. The distance between PU transmitter and SU is 100 km. We define different number of malicious user and observe the impact to detection error rate. The change of transmission power of malicious user is also studied. These two cases is investigated under two hypothesis of Neyman Pearson criterion. The derived results show that increasing number of malicious user can increase probability of error rate. Furthermore, increasing power transmission of malicious user can increase probability of error rate as well.

Cognitive radio technology has become a promising approach to increase the efficiency of spectrum utilization. Since cognitive radio users are vulnerable to malicious attacks, security countermeasures are crucial to the successful deployment of cognitive radio networks in the future. In this paper, we focus on defending against the jamming attack, one of the major threats to cognitive radio networks, where several malicious attackers intend to jam the secondary user's communication link by injecting interference. We model this scenario into a jamming game, and derive the optimal strategy through the Markov decision process approach. Furthermore, a learning scheme is proposed for the secondary user to observe the wireless environment and estimate parameters such as primary users' access pattern and the number of attackers. Finally, simulation results are presented to verify the performance.

We focus on a specific class of denial-of-service (DoS) attacks that is executed through Channel Eviction Triggering (CET), whereby adversary nodes unduly invoke mechanisms inherent in a cognitive radio (CR) network (CRN) operation to protect the licensed users and thus disrupt secondary access to the otherwise idle licensed bands. Skewing the spectrum sensing decision of CRN through sensing misreports is a manifestation of CET attacks. Whereas most studies in the literature focus on making the cooperative sensing more robust against such sensing misreports, we tackle the problem from the novel perspective of incentive alleviation. We distinguish two classes of such DoS attacks, which we refer to as CET and CET-jamming attacks. In the former case, the incentive of adversary CRs is to remove the competition of truthful CRs in accessing the licensed spectral ranges. The latter class of DoS attack deals with scenarios in which the adversary nodes are mainly interested in denying the chances of communication of CRN over primary bands and as such their incentive cannot be modelled by the same utility maximisation model as truthful CRs. We propose a solution for each class of attacks, and our numerical results verify the effectiveness of the proposed CET defence scheme in both cases. Copyright © 2012 John Wiley & Sons, Ltd.

In 1988, SC21, the ISO committee responsible for the Open Systems Interconnection (OSI) Reference Model, determined that it was time to undertake revising ISO 7498-1, The Basic OSI Reference Model. Since the model had been published in 1984, this was in accordance with ISO practice of reviewing and revising standards every five years, and there was good reason for considering the task. Over the intervening five years, the groups developing the OSI protocols had raised many questions about the architecture and these had been answered and documented in the Approved Commentaries [1994]. Many of these commentaries contained information that could be usefully incorporated into the Reference Model itself. In addition, the addendum describing connectionless mode, i.e. datagrams, had been completed several years before and needed to be incorporated. There was also considerable demand for interworking connection-mode and connectionless mode communication, something not supported by the Reference Model or any architecture. Also, when the original version of the Reference Model was frozen about 1983 some aspects of OSI such as the upper layers were not well understood and were only described in the most cursory manner. And while connection-mode and connectionless mode had been brought within OSI, there was no indication as to how broadcast and multicast were to be handled. Thus, the revision might be able to provide a more comprehensive description of these areas.This paper describes how the revision was carried out, describes the changes and additions that were made, considers the effect and contribution this revision has made to our understanding, and describes the outstanding issues that were not addressed by this revision.

The primary user emulation attack (puea) is considered to be one of the common threats in cognitive radio networks (crns). in this problem, an attacker emulates the primary user (pu) signal to deceive other secondary users (sus) and forcing them to leave the white spaces (free spaces) in the spectrum assigned before to the PU. The PUEA is detected and localized using the time-difference-of-arrival (TDOA) localization technique based on stochastic optimization algorithms. Particle swarm optimization (PSO) algorithms are proposed to minimize the cost function provided by the TDOA measurements and to increase the accuracy of the detection. The PSO variants are evolved by changing the parameters of the standard PSO such as inertia weight and acceleration constants. These approaches are presented and compared with the standard PSO in terms of convergence speed and processing time. This paper presents the first study of designing a PSO algorithm suitable for the localization problem and it will be considered as a good guidance for applying the optimization algorithms in wireless positioning techniques. Mean square error (MSE) and cumulative distribution function (CDF) are used as the evaluation metrics to measure the accuracy of the proposed algorithms. Simulation results show that the proposed PSO approaches provide higher accuracy and faster convergence than the standard PSO, social spider optimization (SSO), cuckoo search (CS), firefly optimization (FA) and Taylor series estimation (TSE) methods.

One of the most challenging issues in Cognitive Radio Networks (CRN) is to detect Primary User Emulation Attack (PUEA). In the absence of Primary Users (PU), the attackers mimic PUs’ signal characteristics to fool legitimate Secondary Users (SU) that evacuate the channel for them, in order to use the channel selfishly. Many works have been done to detect PUEA; among them, localization and encryption and so on are the examples. Recently, game theory has been used to detect PUEA. In this paper, a method based on game theory is proposed, that without using any complex calculation and second methods (RSS, GPS and so on), PUEA can be detected. This method is especially proposed for MANET and can be used in any circumstance of CRNs (ad-hoc, centralized, distributed…). It is reliable, with minimum miss detection and no false alarm of PU. Simulation results show that the proposed method has good operations even in dense networks and ultra-dense networks.

In cognitive radio networks, secondary users need to access the licensed spectrum while neither disturbing the transmissions of the primary users nor introducing much interference to other secondary users. Hence, proper power controlling is of paramount importance to ensure efficient system operation. The previous work on power control in cognitive networks mainly focuses on the interactions among a set of secondary users. The primary users' influence is rarely considered. In this paper, a new power control game model is formulated, by including the primary users into the player set of the game. The primary users are rewarded for sharing their licensed spectrum with secondary users by setting a reasonable interference cap for the secondary transmissions. However, we ensure a minimum required Quality of Service (QoS) for primary users by severely penalizing their utility if their required transmission quality is not met. Simultaneously, the secondary users achieve energy efficient transmissions while not introducing much interference to both the primary and other secondary users. We prove that the proposed cognitive power control game has a unique Nash equilibrium (NE). The numerical examples show that the proposed game theoretical power control algorithm can provide the secondary users with energy efficient transmissions and the primary users with reasonable monetary rewards, while not compromising their required QoS.

We investigate opportunistic cooperation between secondary (unlicensed) users and primary (licensed) users in cognitive radio networks. We consider two models for such cooperation. In the first model, called the Cooperative Relay Model, a secondary user cannot transmit its own data concurrently with a primary user. However, it can employ cooperative relaying in order to improve the effective transmission rate of the primary user. In the second model, called the Interference Model, a secondary user is allowed to transmit its data concurrently with a primary user. However, the secondary user can “cooperate” by deferring its transmissions when the primary user is busy. In both models, the secondary users must make intelligent cooperation decisions as they seek to maximize their own throughput subject to average power constraints. The decision options are different during idle and busy periods of the primary user, and the decisions in turn influence the durations of these periods according to a controllable infinite state Markov chain. Such problems can be formulated as constrained Markov decision problems, and conventional solution techniques require either extensive knowledge of the system dynamics or learning based approaches that suffer from large convergence times. However, using a generalized Lyapunov optimization technique, we design a novel greedy and online control algorithm that overcomes these challenges. Remarkably, this algorithm does not require any knowledge of the network arrival rates and is provably optimal.

In a cognitive radio-based network (CRN), secondary users (SUs) opportunistically access underutilized spectrum resources and stop utilizing these resources when licensed or primary users (PUs) reappear. However, this way of opportunistic spectrum access is susceptible to novel vulnerabilities. Recently, a new attack, off-sensing (OS), has shed light on a vulnerability in the Federal Communications Commission (FCC) policy of CRN, which affects the channel utilization of the victim SU by creating an illusion of a PU's presence. However, prior work on OS-attack considers a deterministic approach that is unrealistic and is futile to fortify against conventional defense techniques. In this paper, we propose a new random approach, the random-OS attack, which adapts to realistic scenarios and is difficult to detect using conventional techniques. Then, we model the interaction between the victim SU and attackers as a stochastic zero-sum Markov game and propose a novel safeguard approach based on the Markov decision process to defend the proposed attack, namely hide and seek . Finally, we introduce an OS-attack detection strategy, which utilizes the sensing history to detect the presence of attackers without violating any policy or design constraints and without any networking overhead. Mathematical analysis and extensive simulation results exhibit the superior performance of our proposed work and advent a direction in designing safeguard strategies without amending the current FCC policies.