Enhanced Secure Transmission Against Intelligent Attacks
In this paper, we proposed an enhanced secure scheme for the wireless communication system threatened by an intelligent attacker, which can work in eavesdropping, jamming, and spoofing modes. The conventional secure scheme is to apply Q-learning-based algorithm to reach a Nash equilibrium (NE) in th...
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doaj-aa5f7afae1ee4315be0fa4a3d4ab14b72021-03-29T22:03:48ZengIEEEIEEE Access2169-35362019-01-017535965360210.1109/ACCESS.2019.29124208698241Enhanced Secure Transmission Against Intelligent AttacksChao Li0https://orcid.org/0000-0003-2505-0701Wen Zhou1Kai Yu2Liseng Fan3Junjuan Xia4https://orcid.org/0000-0003-2787-6582School of Computer Science, Guangzhou University, Guangzhou, ChinaCollege of Information Science and Technology, Nanjing Forestry University, Nanjing, ChinaChina Railway Eryuan Engineering Group Co., Ltd., Chengdu, ChinaSchool of Computer Science, Guangzhou University, Guangzhou, ChinaSchool of Computer Science, Guangzhou University, Guangzhou, ChinaIn this paper, we proposed an enhanced secure scheme for the wireless communication system threatened by an intelligent attacker, which can work in eavesdropping, jamming, and spoofing modes. The conventional secure scheme is to apply Q-learning-based algorithm to reach a Nash equilibrium (NE) in the framework of a zero-sum game between the transmitter and attacker, which, however, requires the number of antennas at the transmitter to be much larger than that at the attacker. To overcome this limitation, we first consider the scenario where the attacker can flexibly increase the number of antennas in order to increase the attack rate. By adaptively setting the number of antennas at the transmitter and the legitimate receiver equal to that at the attacker, we then apply the beamforming at the transmitter to suppress the eavesdropping and use the filtering at the receiver to prevent the jamming and spoofing. By incorporating the beamforming and filtering, the benefits of the attacker in this game are efficiently restricted. Furthermore, the Q-learning-based power control strategy is used to reach a new NE. The simulation results have been demonstrated to show that the proposed scheme can suppress the intelligent attack efficiently, which outperforms the conventional scheme in the secrecy performance.https://ieeexplore.ieee.org/document/8698241/Reinforcement learningmodel-freebeamformingzero-sum game |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chao Li Wen Zhou Kai Yu Liseng Fan Junjuan Xia |
spellingShingle |
Chao Li Wen Zhou Kai Yu Liseng Fan Junjuan Xia Enhanced Secure Transmission Against Intelligent Attacks IEEE Access Reinforcement learning model-free beamforming zero-sum game |
author_facet |
Chao Li Wen Zhou Kai Yu Liseng Fan Junjuan Xia |
author_sort |
Chao Li |
title |
Enhanced Secure Transmission Against Intelligent Attacks |
title_short |
Enhanced Secure Transmission Against Intelligent Attacks |
title_full |
Enhanced Secure Transmission Against Intelligent Attacks |
title_fullStr |
Enhanced Secure Transmission Against Intelligent Attacks |
title_full_unstemmed |
Enhanced Secure Transmission Against Intelligent Attacks |
title_sort |
enhanced secure transmission against intelligent attacks |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
In this paper, we proposed an enhanced secure scheme for the wireless communication system threatened by an intelligent attacker, which can work in eavesdropping, jamming, and spoofing modes. The conventional secure scheme is to apply Q-learning-based algorithm to reach a Nash equilibrium (NE) in the framework of a zero-sum game between the transmitter and attacker, which, however, requires the number of antennas at the transmitter to be much larger than that at the attacker. To overcome this limitation, we first consider the scenario where the attacker can flexibly increase the number of antennas in order to increase the attack rate. By adaptively setting the number of antennas at the transmitter and the legitimate receiver equal to that at the attacker, we then apply the beamforming at the transmitter to suppress the eavesdropping and use the filtering at the receiver to prevent the jamming and spoofing. By incorporating the beamforming and filtering, the benefits of the attacker in this game are efficiently restricted. Furthermore, the Q-learning-based power control strategy is used to reach a new NE. The simulation results have been demonstrated to show that the proposed scheme can suppress the intelligent attack efficiently, which outperforms the conventional scheme in the secrecy performance. |
topic |
Reinforcement learning model-free beamforming zero-sum game |
url |
https://ieeexplore.ieee.org/document/8698241/ |
work_keys_str_mv |
AT chaoli enhancedsecuretransmissionagainstintelligentattacks AT wenzhou enhancedsecuretransmissionagainstintelligentattacks AT kaiyu enhancedsecuretransmissionagainstintelligentattacks AT lisengfan enhancedsecuretransmissionagainstintelligentattacks AT junjuanxia enhancedsecuretransmissionagainstintelligentattacks |
_version_ |
1724192303072935936 |