Physical layer security in cognitive radio networks: A beamforming approach

SummarySpectrum sensing in cognitive radio networks (CRNs) is subjected to some security threats such as primary user emulation (PUE) attack and spectrum sensing data falsification (SSDF) attack. In PUE attack, a malicious user (MUPUE) transmits an emulated primary signal throughout the spectrum sensing interval to secondary users (SUs) to forestall them from accessing the primary user (PU) spectrum bands. In SSDF attack, malicious users (MUSSDF) intentionally report false sensing decisions to the fusion center (FC) to influence the overall decision. While most of the existing literatures have studied the effects of these 2 types of attacks separately, the present paper evaluates the secondary network performance in terms of throughput under both the PUE and SSDF attacks with improved energy detectors (IEDs) where SU's spectrum access is hybrid, ie, either in overlay or in underlay mode. An analytical expression on throughput of SU under the simultaneous influence of both of these attacks is developed. Impact of several parameters such as IED parameter, attacker probabilities, and attacker strength on the throughput of SU is investigated. Performance of the present scheme is also compared with only PUE and only SSDF attacks. A simulation test bed is developed in MATLAB to validate our analytical results.

The Cognitive Radio (CR) is a fully-reconfigurable wireless device that can intelligently sense, manage, and exploit temporarily-vacant licensed spectrum bands during the absence of incumbent users. Broadly, the IEEE 802.22 is the first complete Wireless Regional Area Network (WRAN) standard that utilizes CR technology in the opportunistic access of white spaces in the television (TV) bands. Intrinsically, the increased CR networks' vulnerabilities alongside the experienced growth of attackers' capabilities, creates a strain on CR network designers to act accordingly. However, most of the existing efforts solely examined the issues of Denial-of-Service (DoS) attacks of IEEE 802.22 networks, such as Primary User Emulation (PUE) attack and Spectrum Sensing Data Falsification (SSDF) attack, each treated in isolation. One main challenge in CR network security domain is to efficiently represent possible simultaneous multiple security threats, and assess their effects. Unlike the previous works, this paper addresses the aforementioned challenge through using the holistic approach of assessing the combined effect of simultaneous multiple DoS attacks. The Bayesian Attack Graph (BAG) model is utilized in this paper to capture the probabilistic dependencies among IEEE 802.22 DoS threat-environment and known vulnerabilities. The simulation results indicate up to 51.3% increase in the probability of DoS in IEEE 802.22 networks considering simultaneous multiple attacks in comparison to the most severe sole attack. Finally, the paper introduces the BAG model as a feasible CR vulnerability metric that can facilitate the creation of a security tightening plan.

Cooperative spectrum sensing (CSS) has been considered as a powerful approach to improve the utilization of scarce radio spectrum resources. However, spectrum sensing data falsification (SSDF) can hugely degrade the achievable detection accuracy of the primary user (PU) channel status in cognitive radio (CR) networks and thus requires learning from past CR network environments. Using the supervised learning (SL) method, a large number of labels about the real state of the channel availability need to be acquired. In a practical setting, however, obtaining off-the-shelf label data is a challenging undertaking because it necessitates cooperation among PUs and secondary users (SUs) and increases communication overhead in CR networks. Motivated by this, we solve the issue of label data scarcity in CR networks in this paper, and propose a semi-supervised learning-enabled two-stage framework for CSS against SSDF attack, in which combines the superior performance of semi-supervised support vector machine (S3VM) and the fast convergence of K-means. At last, simulation results demonstrate that S3VM is equipped with a greater detection performance than support vector data description (SVDD), and the same detection performance as support vector machine (SVM), especially it also the robustness and excellent performance using a small amount of labeled data.

By enpowering the capability of CR, sensor nodes in CRSNs can sense the availability of licensed channels and access the idle channels for data transmission. However, due to channel fading and shadowing, spectrum sensing by individual sensor node has inevitable sensing errors, which adversely impact the performance of both the CRSN and the primary users (PUs) of the licensed channels. To overcome the limitation of individual spectrum sensing, collaborative spectrum sensing is generally employed in CRSNs to improve the spectrum sensing accuracy [1, 2]. However, since sensor nodes may be compromised by adversaries, these nodes can send false sensing results, making CRSNs vulnerable to spectrum sensing data falsification (SSDF) attack [3–5]. In SSDF attacks, compromised nodes may operate independently or collaboratively to mislead the channel availability decision, which can significantly reduce spectrum utilization and degrade overall network performance [6]. To resist SSDF attacks, a number of countermeasures, including trust/reputation based approaches [7–10], abnormal statistical-behavior detection based approaches [11–15] and clustering based approaches [16, 17], have been proposed in cognitive radio networks (CRNs), which could be applied into CRSNs. But few of the related works consider energy efficiency in SSDF attack countermeasure design [18, 19].

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.

Spectrum sensing data falsification (SSDF) attacks represent a major challenge for cooperative spectrum sensing (CSS) in cognitive radio (CR) networks. In an SSDF attack, a malicious user or many malicious users send false sensing results to the fusion center (FC) to mislead the global decision about spectrum occupancy. Thus, an SSDF attack degrades the achievable detection accuracy, throughput, and energy efficiency of CR networks (CRNs). In this paper, a novel attacker-identification algorithm is proposed that is able to skillfully detect attackers and reject their reported results. Moreover, we provide a novel attacker-punishment algorithm that aims at punishing attackers by lowering their individual energy efficiency, motivating them either to quit sending false results or leave the network. Both algorithms are based on a novel assessment strategy of the sensing performance of each user. The proposed strategy is called delivery-based assessment, which relies on the delivery of the transmitted data to evaluate the made global decision and the individual reports. Mathematical analysis and simulation results show promising performance of both algorithms compared with previous works, particularly when then the number of attackers is very large.

Promising to significantly improve spectrum utilization, cognitive radio networks (CRNs) have attracted a great attention in the literature. Nevertheless, a new security threat known as the primary user emulation (PUE) attack raises a great challenge to CRNs. The PUE attack is unique to CRNs and can cause severe denial of service (DoS) to CRNs. In this paper, we propose a novel PUE detection system, termed Signal activity Pattern Acquisition and Reconstruction System. Different from current solutions of PUE detection, the proposed system does not need any a priori knowledge of primary users (PUs), and has no limitation on the type of PUs that are applicable. It acquires the activity pattern of a signal through spectrum sensing, such as the ON and OFF periods of the signal. Then it reconstructs the observed signal activity pattern through a reconstruction model. By examining the reconstruction error, the proposed system can smartly distinguish a signal activity pattern of a PU from a signal activity pattern of an attacker. Numerical results show that the proposed system has excellent performance in detecting PUE attacks.

Most of the frequency spectrum bands have already been licensed, and the licensed spectrum is not being utilized efficiently. Cognitive Radio Networks (CRNs) are the kind of full duplex radio that automatically altered its transmission or reception parameters, in such a way that the entire wireless communication network of which it is a node communicates efficiently, while avoiding interference with primary or secondary users. In this chapter, the authors introduce the concept of security threats that may pose a serious attack in CRN. Due to the unique characteristics of CRN, such network is highly vulnerable to security attacks compared to wireless network or infrastructure-based wireless network. The main objective of this chapter is to assist CR designers and the CR application engineers to consider the security factors in the early development stage of CR techniques. Challenges and various security issues are explored with respect to OSI (Open Systems Interconnection) reference model. Various possible and attacks are discussed broadly and respective solutions are also proposed by this chapter. Different architectures and models are also explained, and compared with the existing models.

We study security issues that may pose a serious threat to the spectrum sensing for wireless cognitive radio networks. In particular, we focus on two of the main security threats: Incumbent Emulation (IE) attacks and Spectrum Sensing Data Falsification (SSDF) attacks. We develop a Distributed Spectrum Sensing (DSS) algorithm to defend against each of the two attacks in a two-tier cognitive radio network. For the IE attacks, we develop a user verification scheme, called localization-based defense. For the SSDF attacks, we propose an optimal nonlinear cooperation scheme, which minimizes the interference to the primary emulation attack. To take the more realistic scenarios into account, we characterize the DSS algorithm as an M-ary Hypotheses problem, and formulate it as an optimization problem by introducing a deflection coefficient. The simulation results evaluate our proposed optimal nonlinear cooperation scheme and validate the significant cooperative gain achieved.

Malicious users can exploit vulnerabilities in cognitive radio networks (CRNs) and cause heavy performance degradation by denial of service (DoS) attacks. During operation, cognitive radios (CRs) spend a considerable amount of time to identify idle (free) channels for transmission. In addition, CRs also need additional security mechanisms to prevent malicious attacks. Proactive model predictive control (MPC)-based medium access control (MAC) protocols for CRs can quicken the idle channel identification by predicting future states of channels in advance. This provides enough time for CRs to carry out other calculations like DoS attack detection. However, such external detection techniques use additional power that makes them inappropriate for energy constrained applications. As a solution, this paper proposes a proactive learning-based MAC protocol (PROLEMus) that shows immunity to two prominent CR-based DoS attacks, namely, primary user emulation attack (PUEA) and spectrum sensing data falsification (SSDF) attack, without any external detection mechanism. PROLEMus shows an average of 6.2%, 8.9%, and 12.4% improvement in channel utilization, backoff rate, and sensing delay, respectively, with low prediction errors (≤1.8%) saving 19.65% energy, when compared to recently proposed MAC protocols like ProMAC aided with additional DoS attack detection mechanism.

In cooperative spectrum sensing, malicious user (MU) sends tampered sensing result in spectrum sensing to pose threats to cognitive radio networks, which is called spectrum sensing data falsification (SSDF) attack. Many existing MU detection schemes are designed for the narrow-band environment. However, Research on MU detection schemes for compressive sensing (CS), which has some huge advantages in sensing wide-band spectrum, is just beginning. Because of the high complex of reconstruction algorithms, it's necessary to identify potential MU and then exclude the MU before reconstructing the original signal in CS. In this paper, we consider attack and defense behavior in CS and propose a density-based MU detection (DBMUD) with a trusted user to distinguish MU precisely. DBMUD maintains reliability even if MUs are a majority. Simulation results demonstrate that DBMUD performs well in fighting against SSDF attack.

With the rapidly growing number of wireless users and systems more frequency bands are needed, which led to the spectrum scarcity problems. Cognitive radio (CR) is an emerging technology that enables the dynamic spectrum access by optimizing the usage of the available spectrum through giving the unlicensed Secondary Users (SU) the opportunity to exploit the spectrum without causing any interference with the Primary Users’ (PU) usage. Security threats is one of the substantial problems issues that must be addressed in CR networks. Primary User Emulation (PUE) attacks are one of the main threats in deploying CRN, where the Malicious Users (MU) mimic the PU signal to confuse the other SUs and corrupt the spectrum sensing process. In this paper, a hybrid Genetic Whale Optimization Algorithm (GWOA) is proposed to optimize the spectrum utilization and to combat the PUE attacks. GWOA integrates the crossover and mutation operations with WOA algorithm to reach the balance between exploitation and exploration phases, to find the optimal solution based on a fitness function. The simulations results show promising performance of GWOA, when compared with recent detection algorithms.

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

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.

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.