Securing Collaborative Spectrum Sensing Against Malicious Attackers in Cognitive Radio Networks

Sharifi, Abbas Ali; Niya, Javad Musevi

doi:10.1007/s11277-016-3331-8

Cited by 20 publications

(8 citation statements)

References 20 publications

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“…If those AIDs launch a simple attack characteristic, they can be easily identified by the decision fusion criteria at the FC. In fact, such a type of always attack has been extensively studied in [32][33][34][35][36][37][38]; the defense schemes in these references have shown satisfactory performance in some settings; however, when confronting the sophisticated attack probability (ie, a pair of attack probability 1 and 0 varying from 0 to 1), most of them are bound to fail.…”

Section: Sensing Data Falsification Attack Modelmentioning

confidence: 99%

Sequential fusion to defend against sensing data falsification attack for cognitive Internet of Things

Song

et al. 2020

ETRI Journal

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Internet of Things (IoT) is considered the future network to support wireless communications. To realize an IoT network, sufficient spectrum should be allocated for the rapidly increasing IoT devices. Through cognitive radio, unlicensed IoT devices exploit cooperative spectrum sensing (CSS) to opportunistically access a licensed spectrum without causing harmful interference to licensed primary users (PUs), thereby effectively improving the spectrum utilization. However, an open access cognitive IoT allows abnormal IoT devices to undermine the CSS process. Herein, we first establish a hard‐combining attack model according to the malicious behavior of falsifying sensing data. Subsequently, we propose a weighted sequential hypothesis test (WSHT) to increase the PU detection accuracy and decrease the sampling number, which comprises the data transmission status‐trust evaluation mechanism, sensing data availability, and sequential hypothesis test. Finally, simulation results show that when various attacks are encountered, the requirements of the WSHT are less than those of the conventional WSHT for a better detection performance.

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Section: Sensing Data Falsification Attack Modelmentioning

confidence: 99%

Sequential fusion to defend against sensing data falsification attack for cognitive Internet of Things

Song

et al. 2020

ETRI Journal

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“…AF attack boils down to a combination of AY and AN attack ( α = β = 1). Furthermore, as for attack strategy, α = β is too ideal to be achieved in [ 9 ] [ 28 ] etc. Such a view totally overlooks the fact that FAA and MDA are independent.…”

Section: System Modelmentioning

confidence: 99%

“…On the basis of WSPRT, [ 8 ] analyzed the CSS performance when there exist attackers based on the cumulative reputation, and proposed an effective scheme by employing Orthogonalized Gnanadesikan-Kettenring (OGK) to improve the robustness of CRNs. In [ 9 ], Sharifi AA and Niya JM proposed an attack-aware CSS (ACSS) method to estimate the credit value of each CR user and identify attackers along with their attack strategies. The similar work was presented in [ 10 ], an attack-aware WSPRT algorithm estimates the attack extension factor in a network, based on the standard deviation of received sensing reports for improving the CSS performance.…”

Section: Introductionmentioning

confidence: 99%

Sequential cooperative spectrum sensing in the presence of dynamic Byzantine attack for mobile networks

Song

et al. 2018

PLoS ONE

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Cooperative spectrum sensing (CSS) is envisaged as a powerful approach to improve the utilization of scarce radio spectrum resources, but it is threatened by Byzantine attack. Byzantine attack has been becoming a popular research topic in both academia and industry due to the demanding requirements of security. Extensive research mainly aims at mitigating the negative effect of Byzantine attack on CSS, but with some strong assumptions, such as attackers are in minority or trusted node(s) exist for data fusion, while paying little attention to a mobile scenario. This paper focuses on the issue of designing a general and reliable reference for CSS in a mobile network. Instead of the previously simplified attack, we develop a generic Byzantine attack model from sophisticated behaviors to conduct various attack strategies and derive the condition of which Byzantine attack makes the fusion center (FC) blind. Specifically, we propose a robust sequential CSS (SCSS) against dynamic Byzantine attack. Our proposed method solves the unreliability of the FC by means of delivery-based assessment to check consistency of individual sensing report, and innovatively reuses the sensing information from Byzantines via a novel weight allocation mechanism. Furthermore, trust value (TrV) ranking is exploited to proceed with a sequential test which generates a more accurate decision about the presence of phenomenon with fewer samples. Lastly, we carry out simulations on comparison of existing data fusion technologies and SCSS under dynamic Byzantine attack, and results verify the theoretical analysis and effectiveness of our proposed approach. We also conduct numerical analyses to demonstrate explicit impacts of secondary user (SU) density and mobility on the performance of SCSS.

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“…Spectrum sensing is the premise of the successful implementation of CR [6,7]. Among various spectrum sensing techniques, energy detection is widely used due to its low implementation complexity and without any prior knowledge about signal features [8].…”

Section: Introductionmentioning

confidence: 99%

An algorithm based on evidence theory and fuzzy entropy to defend against SSDF

Bai

Tian

2020

J. of Syst. Eng. Electron.

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In cognitive radio networks, spectrum sensing is one of the most important functions to identify available spectrum for improving the spectrum utilization. Due to the open characteristic of the wireless electromagnetic environment, the wireless network is vulnerable to be attacked by malicious users (MUs), and spectrum sensing data falsification (SSDF) attack is one of the most harmful attacks on spectrum sensing performance. In this article, an algorithm based on the evidence theory and fuzzy entropy is proposed to resist SSDF attacks. In this algorithm, secondary users (SUs) obtain the corresponding degree of membership function and basic probability assignment function based on the local energy detection result. The new conflicting coefficient is calculated based on the evidence distance and classical conflicting coefficient, and the conflicting weight of the evidence is obtained. The fuzzy weight is calculated by the fuzzy entropy. The credibility weight is obtained by updating the credibility. On this basis, the probability assignment function of the evidence is corrected, and the final result is obtained by using the fusion formula. Simulation results show that the proposed algorithm has a higher detection probability and lower false alarm probability than other algorithms. It can effectively defend against SSDF attacks and improve the performance of spectrum sensing.

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Securing Collaborative Spectrum Sensing Against Malicious Attackers in Cognitive Radio Networks

Cited by 20 publications

References 20 publications

Sequential fusion to defend against sensing data falsification attack for cognitive Internet of Things

Sequential fusion to defend against sensing data falsification attack for cognitive Internet of Things

Sequential cooperative spectrum sensing in the presence of dynamic Byzantine attack for mobile networks

An algorithm based on evidence theory and fuzzy entropy to defend against SSDF

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