On the Trade-Off Between Security and Energy Efficiency in Cooperative Spectrum Sensing for Cognitive Radio

Althunibat, Saud; Sucasas, Victor; Marques, Hugo; Rodriguez, Jonathan; Tafazolli, Rahim; Granelli, Fabrizio

doi:10.1109/lcomm.2013.062113.130759

Cited by 44 publications

(71 citation statements)

References 10 publications

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“…The literature [23] has pointed out that data fusion schemes become completely incapable, and no reputation-based fusion scheme can achieve any performance gain when the number of attackers exceeds a certain fraction in the CRN. If the number of independent attackers is greater than half of the total users, the FC will be rendered "blind".…”

Section: Calculation Of the Cr Valuementioning

confidence: 99%

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Comprehensive Reputation-Based Security Mechanism against Dynamic SSDF Attack in Cognitive Radio Networks

Zhang

2016

Symmetry

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Abstract:Collaborative spectrum sensing (CSS) was envisioned to improve the reliability of spectrum sensing in centralized cognitive radio networks (CRNs). However, secondary users (SUs)' changeable environment and ease of compromise make CSS vulnerable to security threats, which further mislead the global decision making and degrade the overall performance. A popular attack in CSS is the called spectrum sensing data falsification (SSDF) attack. In the SSDF attack, malicious cognitive users (MUs) send false sensing results to the fusion center, which significantly degrades detection accuracy. In this paper, a comprehensive reputation-based security mechanism against dynamic SSDF attack for CRNs is proposed. In the mechanism, the reliability of SUs in collaborative sensing is measured with comprehensive reputation values in accordance with the SUs' current and historical sensing behaviors. Meanwhile a punishment strategy is presented to revise the reputation, in which a reward factor and a penalty factor are introduced to encourage SUs to engage in positive and honest sensing activities. The whole mechanism focuses on ensuring the correctness of the global decision continuously. Specifically, the proposed security scheme can effectively alleviate the effect of users' malicious behaviors on network decision making, which contributes greatly to enhancing the fairness and robustness of CRNs. Considering that the attack strategy adopted by MUs has been gradually transforming from simplicity, fixedness and singleness into complexity, dynamic and crypticity, we introduce two dynamic behavior patterns (true to false and then to true (TFT) and false to true and then to false (FTF)) to further validate the effectiveness of our proposed defense mechanism. Abundant simulation results verify the rationality and validity of our proposed mechanism.

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Section: Calculation Of the Cr Valuementioning

confidence: 99%

“…For simplicity, we employ the majority fusion rule as an example in this paper, which is proven to be relatively ideal in both detection accuracy and energy efficiency [23].…”

Section: Measurement Combining Stagementioning

confidence: 99%

Comprehensive Reputation-Based Security Mechanism against Dynamic SSDF Attack in Cognitive Radio Networks

Zhang

2016

Symmetry

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“…When the instantaneous channel coefficients are not available, an average EE is defined as the ratio of the average transmission rate (or throughput) to the average transmit and consumed circuitry power (or energy) [2,7,[39][40][41]47]. For example, for an SC SISO scenario where an SU overlays a PU and concurrent transmission is required, the average EE is defined as [40] …”

Section: Energy Efficiency (Ee) Metricsmentioning

confidence: 99%

“…As can be seen, the numerator of (11) accounts for the average number of transmit bits, while the denominator accounts for the total average consumed energy. In a cooperative sensing scheme, the average EE can be defined to account for the total energy consumed by all SUs in the sensing process as well as the energy required for transmission as [2] …”

Section: Energy Efficiency (Ee) Metricsmentioning

confidence: 99%

“…This authentication can be achieved by transmitting extra bits for cryptographic purposes; however, additional energy has to be spent in these overhead bits. Then, the number of the security bits has to be optimized in order to maximize η EE,av in (12) [2]. An average EE metric that captures the influence of malicious users is formulated, i.e., the malicious users tend to increase the false alarm probability, which decreases the successful amount of bits transmitted, and hence, reduces the EE.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

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Energy-Aware Cognitive Radio Systems

Bedeer

Amin

Dobre

et al. 2016

Energy Management in Wireless Cellular and Ad-Hoc Networks

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The concept of energy-aware communications has spurred the interest of the research community in the most recent years due to various environmental and economical reasons. It becomes indispensable for wireless communication systems to shift their resource allocation problems from optimizing traditional metrics, such as throughput and latency, to an environmental-friendly energy metric. Although cognitive radio systems introduce spectrum efficient usage techniques, they employ new complex technologies for spectrum sensing and sharing that consume extra energy to compensate for overhead and feedback costs. Considering an adequate energy efficiency metric-that takes into account the transmit power consumption, circuitry power, and signaling overhead-is of momentous importance such that optimal resource allocations in cognitive radio systems reduce the energy consumption. A literature survey of recent energy-efficient based resource allocations schemes is presented for cognitive radio systems. The energy efficiency performances of these schemes are analyzed and evaluated under power budget, co-channel and adjacentchannel interferences, channel estimation errors, quality-of-service, and/or fairness constraints. Finally, the opportunities and challenges of energy-aware design for cognitive radio systems are discussed.

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Lightweight security against combined IE and SSDF attacks in cooperative spectrum sensing for cognitive radio networks

Sucasas

Althunibat

Radwan

et al. 2015

Security Comm Networks

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Cognitive radio is envisaged as a promising solution to cope with the problem of spectrum scarcity. In cognitive radio networks, users can sense the medium and opportunistically use available frequency bands. Users can cooperate in order to increase the reliability of the sensing process, which is called cooperative spectrum sensing (CSS). However, cooperative paradigms are threatened by the behavior of malicious users. Two types of attacks represent the main threats for CSS network operation, namely, spectrum sensing data falsification (SSDF) and incumbent emulation (IE). These two types of attacks have received a considerable amount of attention in the literature, but they have always been studied separately. In this paper, we propose a novel mechanism based on lightweight cryptography that considers both SSDF and IE attacks combined in a CSS network for the first time. Lightweight cryptography, in contrast to previous techniques used (such as intrusion detection or reputation systems), provides higher resilience to such attacks when a high number of mobile malicious users exist, providing better energy efficiency. Analytical and simulation results show the outperformance of the proposed algorithm compared with previous mechanisms, in terms of lower false alarm probability (i.e., higher chance of using the free frequency bands) and hence better energy ratings. Copyright © 2015 John Wiley & Sons, Ltd.

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On the Trade-Off Between Security and Energy Efficiency in Cooperative Spectrum Sensing for Cognitive Radio

Cited by 44 publications

References 10 publications

Comprehensive Reputation-Based Security Mechanism against Dynamic SSDF Attack in Cognitive Radio Networks

Comprehensive Reputation-Based Security Mechanism against Dynamic SSDF Attack in Cognitive Radio Networks

Energy-Aware Cognitive Radio Systems

Lightweight security against combined IE and SSDF attacks in cooperative spectrum sensing for cognitive radio networks

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