面向智能无人通信系统的因果性对抗攻击生成算法

doi:10.11959/j.issn.1000-436x.2024036

Abstract

Abstract:

A causality adversarial attack generation algorithm was proposed in response to the causality issue of gradient-based adversarial attack generation algorithms in practical communication system.The sequential input-output features and temporal memory capability of long short-term memory networks were utilized to extract the temporal correlation of communication signals while satisfying practical causality constraints, and enhance the adversarial attack performance against unmanned communication systems.Simulation results demonstrate that the proposed algorithm outperforms existing causality adversarial attack algorithms, such as universal adversarial perturbation, under identical conditions.

Key words: intelligent communication system, adversarial attack, deep learning, causal system, long short-term memory network

CLC Number:

TN911

Shuwen YU, Wei XU, Jiacheng YAO. Causality adversarial attack generation algorithm for intelligent unmanned communication system[J]. Journal on Communications, 2024, 45(1): 54-62.

Figures/Tables 9

JNR/dB	迭代次数	迭代步长
-30	1	$ϵ_{- 30 dB}$
-26	1	$0.8 ϵ_{- 26 dB}$
-22	1	$ϵ_{- 22 dB}$
-18	3	$ϵ_{- 18 dB}$
-14	18	$0.2 ϵ_{- 14 dB}$
-10	20	$0.3 ϵ_{- 10 dB}$
-6	20	$0.5 ϵ_{- 6 dB}$
-2	17	$0.7 ϵ_{- 2 dB}$
2	19	$0.7 ϵ_{2 dB}$
6	18	$0.9 ϵ_{6 dB}$

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