基于审计博弈的安全协作频谱感知方案

doi:10.11959/j.issn.1000-436x.2023238

Abstract

Abstract:

To defend against poisoning attacks and free-riding attacks conducted by malicious sensing terminals in crowd sensing-based collaborative spectrum sensing (CCSS), a novel audit game-based defense scheme was proposed, which combined the pre-deterrence and post-punishment mechanisms.Firstly, considering the audit budget constraint, a mixed-strategy audit game model under incomplete information was designed, which set a penalty strategy to deter malicious collaborators before spectrum sensing, and audited and punished them after the fusion of sensing data.Then, a lightweight audit chain model with on-chain and off-chain collaboration was designed, in which audit evidence was stored in an off-chain data warehouse and its metadata was publicly published on the blockchain.Furthermore, a distributed and intelligent audit algorithm based on reinforcement learning was devised to adaptively seek the approximate mix-strategy equilibrium of the audit game.Simulation results demonstrate that the proposed scheme can quickly obtain the stable and approximately optimal audit strategies and actively suppress the poisoning and free-riding behaviors of malicious collaborators, in comparison with conventional schemes.

Key words: collaborative spectrum sensing, audit game, security, blockchain, reinforcement learning

CLC Number:

TN92

Yuntao WANG, Zhou SU, Qichao XU, Yiliang LIU, Haixia PENG, Hao LUAN. Secure and collaborative spectrum sensing scheme based on audit game[J]. Journal on Communications, 2023, 44(12): 1-14.

Figures/Tables 9

模型	参数	数值
信道模型^[6]	ι	3.7
	d ₀/m	1
	σ_n/dB	5.5
	P₀/dBm	90
审计模型参数	$ϖ_{1}, ϖ_{2}, ϖ_{3}$	$\frac{1}{3}$
	λ_c	3
	ϕ	3
	λ_r	25
	c	1
	ε	0.001
	x_max	1
	y_max	5.5
	Z	10
	V	11
	χ	$\frac{3}{55}$
PHC模型	$η_{1}, η_{2}$	0.7
	$γ_{1}, γ_{2}$	0.8
	$δ_{1}^{l}, δ_{2}^{l}$	$\frac{1}{50 + \frac{t}{50}}$
	$δ_{1}^{h}, δ_{2}^{h}$	$\frac{2}{50 + \frac{t}{50}}$

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