Security-aware Cooperative Caching via Deep Reinforcement Learning in Fog Radio Access Networks

Qi Chang; Baotian Fan; Yanxiang Jiang; Fu Chun Zheng; Mehdi Bennis; Xiaohu You

doi:10.1109/GCWkshps56602.2022.10008667

Security-aware Cooperative Caching via Deep Reinforcement Learning in Fog Radio Access Networks

Qi Chang
, Baotian Fan^*
, Yanxiang Jiang
, Fu Chun Zheng
, Mehdi Bennis
, Xiaohu You

^*Corresponding author for this work

Southeast University, Nanjing
Harbin Institute of Technology Shenzhen
University of Oulu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, the cooperative caching problem with security guarantees in fog radio access networks (F-RANs) is investigated. To avoid file retransmission, we propose a security prediction model by training a long short-term memory (LSTM) network, which leverages the file integrity and malicious requests to determine which access points can be trusted. To minimize the file transmission delay, we formulate a cooperative caching optimization problem under the consideration of security risk. In view of the complexity of the formulated problem, we employ a soft-actor-critic (SAC) based deep reinforcement learning (DRL) algorithm. Simulation results show that our proposed scheme achieves significant performance improvement over the baseline.

Original language	English
Title of host publication	2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1742-1747
Number of pages	6
ISBN (Electronic)	9781665459754
DOIs	https://doi.org/10.1109/GCWkshps56602.2022.10008667
State	Published - 2022
Externally published	Yes
Event	2022 IEEE Globecom Workshops, GLOBECOM Workshop 2022 - Rio de Janeiro, Brazil Duration: 4 Dec 2022 → 8 Dec 2022

Publication series

Name	2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings

Conference

Conference	2022 IEEE Globecom Workshops, GLOBECOM Workshop 2022
Country/Territory	Brazil
City	Rio de Janeiro
Period	4/12/22 → 8/12/22

Keywords

Fog radio access networks
cooperative caching
deep reinforcement learning
security prediction

Access to Document

10.1109/GCWkshps56602.2022.10008667

Cite this

Chang, Q., Fan, B., Jiang, Y., Zheng, F. C., Bennis, M., & You, X. (2022). Security-aware Cooperative Caching via Deep Reinforcement Learning in Fog Radio Access Networks. In 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings (pp. 1742-1747). (2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCWkshps56602.2022.10008667

@inproceedings{3511793b956040bbb8599e3deef8a2d2,

title = "Security-aware Cooperative Caching via Deep Reinforcement Learning in Fog Radio Access Networks",

abstract = "In this paper, the cooperative caching problem with security guarantees in fog radio access networks (F-RANs) is investigated. To avoid file retransmission, we propose a security prediction model by training a long short-term memory (LSTM) network, which leverages the file integrity and malicious requests to determine which access points can be trusted. To minimize the file transmission delay, we formulate a cooperative caching optimization problem under the consideration of security risk. In view of the complexity of the formulated problem, we employ a soft-actor-critic (SAC) based deep reinforcement learning (DRL) algorithm. Simulation results show that our proposed scheme achieves significant performance improvement over the baseline.",

keywords = "Fog radio access networks, cooperative caching, deep reinforcement learning, security prediction",

author = "Qi Chang and Baotian Fan and Yanxiang Jiang and Zheng, \{Fu Chun\} and Mehdi Bennis and Xiaohu You",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Globecom Workshops, GLOBECOM Workshop 2022 ; Conference date: 04-12-2022 Through 08-12-2022",

year = "2022",

doi = "10.1109/GCWkshps56602.2022.10008667",

language = "英语",

series = "2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1742--1747",

booktitle = "2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings",

address = "美国",

}

Chang, Q, Fan, B, Jiang, Y, Zheng, FC, Bennis, M & You, X 2022, Security-aware Cooperative Caching via Deep Reinforcement Learning in Fog Radio Access Networks. in 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings. 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1742-1747, 2022 IEEE Globecom Workshops, GLOBECOM Workshop 2022, Rio de Janeiro, Brazil, 4/12/22. https://doi.org/10.1109/GCWkshps56602.2022.10008667

Security-aware Cooperative Caching via Deep Reinforcement Learning in Fog Radio Access Networks. / Chang, Qi; Fan, Baotian; Jiang, Yanxiang et al.
2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1742-1747 (2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Security-aware Cooperative Caching via Deep Reinforcement Learning in Fog Radio Access Networks

AU - Chang, Qi

AU - Fan, Baotian

AU - Jiang, Yanxiang

AU - Zheng, Fu Chun

AU - Bennis, Mehdi

AU - You, Xiaohu

PY - 2022

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N2 - In this paper, the cooperative caching problem with security guarantees in fog radio access networks (F-RANs) is investigated. To avoid file retransmission, we propose a security prediction model by training a long short-term memory (LSTM) network, which leverages the file integrity and malicious requests to determine which access points can be trusted. To minimize the file transmission delay, we formulate a cooperative caching optimization problem under the consideration of security risk. In view of the complexity of the formulated problem, we employ a soft-actor-critic (SAC) based deep reinforcement learning (DRL) algorithm. Simulation results show that our proposed scheme achieves significant performance improvement over the baseline.

AB - In this paper, the cooperative caching problem with security guarantees in fog radio access networks (F-RANs) is investigated. To avoid file retransmission, we propose a security prediction model by training a long short-term memory (LSTM) network, which leverages the file integrity and malicious requests to determine which access points can be trusted. To minimize the file transmission delay, we formulate a cooperative caching optimization problem under the consideration of security risk. In view of the complexity of the formulated problem, we employ a soft-actor-critic (SAC) based deep reinforcement learning (DRL) algorithm. Simulation results show that our proposed scheme achieves significant performance improvement over the baseline.

KW - Fog radio access networks

KW - cooperative caching

KW - deep reinforcement learning

KW - security prediction

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DO - 10.1109/GCWkshps56602.2022.10008667

M3 - 会议稿件

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BT - 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings

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Chang Q, Fan B, Jiang Y, Zheng FC, Bennis M, You X. Security-aware Cooperative Caching via Deep Reinforcement Learning in Fog Radio Access Networks. In 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1742-1747. (2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings). doi: 10.1109/GCWkshps56602.2022.10008667

Security-aware Cooperative Caching via Deep Reinforcement Learning in Fog Radio Access Networks

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Keywords

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