TY - JOUR
T1 - Machine learning-based security-aware spatial modulation for heterogeneous radio-optical networks
AU - Khadr, Monette H.
AU - Elgala, Hany
AU - Rahaim, Michael
AU - Khreishah, Abdallah
AU - Ayyash, Moussa
AU - Little, Thomas
N1 - Funding Information:
Data accessibility. The codes used to create the dataset, train the ML algorithms and analyse the data can be accessed at https://github.com/ounety/ML-SASM to be reused under the CC BY licence. For more details, see https://creativecommmons.org/licenses/by/4.0/. Authors’ contributions. All authors contributed to the analysis and writing of the manuscript, tested the code and gave final approval for publication. Competing interests. We declare we have no competing interests. Funding. The authors are grateful for partial support by the National Science Foundation (NSF) under grant no. ECCS-1331018 and the Engineering Research Centers Program of the NSF under NSF Cooperative Agreement no. EEC-0812056. Acknowledgements. The authors would like to sincerely thank the board member and referees whose comments helped improve and clarify this manuscript.
Publisher Copyright:
© 2021 The Author(s).
PY - 2021/4/7
Y1 - 2021/4/7
N2 - In this article, we propose a physical layer security (PLS) technique, namely security-aware spatial modulation (SA-SM), in a multiple-input multiple-output-based heterogeneous network, wherein both optical wireless communications and radio-frequency (RF) technologies coexist. In SA-SM, the time-domain signal is altered prior to transmission using a key at the physical layer for combating eavesdropping. Unlike conventional PLS techniques, SA-SM does not rely on channel characteristics for securing the information, as its perception is self-imposed, which allows its adoption in radio-optical networks. Additionally, a novel periodical key selection algorithm is proposed. Instead of having multiple keys stored in the nodes, by using off-the-shelf and low-complexity machine learning (ML) methods, including a support vector machine, logistic regression and a single-layer neural network, SA-SM nodes can estimate the used key. Results show that a positive secrecy capacity can be achieved for both the RF and optical links by using 1000 different keys, with a minimal signal-to-noise ratio penalty of less than 5 dB for the legitimate user using SA-SM versus conventional transmission at a bit-error-rate of 10-4. The analysis also includes computational time and classification accuracy evaluation of the various proposed ML techniques using different hardware architectures.
AB - In this article, we propose a physical layer security (PLS) technique, namely security-aware spatial modulation (SA-SM), in a multiple-input multiple-output-based heterogeneous network, wherein both optical wireless communications and radio-frequency (RF) technologies coexist. In SA-SM, the time-domain signal is altered prior to transmission using a key at the physical layer for combating eavesdropping. Unlike conventional PLS techniques, SA-SM does not rely on channel characteristics for securing the information, as its perception is self-imposed, which allows its adoption in radio-optical networks. Additionally, a novel periodical key selection algorithm is proposed. Instead of having multiple keys stored in the nodes, by using off-the-shelf and low-complexity machine learning (ML) methods, including a support vector machine, logistic regression and a single-layer neural network, SA-SM nodes can estimate the used key. Results show that a positive secrecy capacity can be achieved for both the RF and optical links by using 1000 different keys, with a minimal signal-to-noise ratio penalty of less than 5 dB for the legitimate user using SA-SM versus conventional transmission at a bit-error-rate of 10-4. The analysis also includes computational time and classification accuracy evaluation of the various proposed ML techniques using different hardware architectures.
KW - heterogeneous networks
KW - machine learning
KW - optical wireless communications
KW - physical layer security
KW - wireless communications
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U2 - 10.1098/rspa.2020.0889
DO - 10.1098/rspa.2020.0889
M3 - Article
AN - SCOPUS:85106565426
SN - 1364-5021
VL - 477
JO - Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
JF - Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
IS - 2248
M1 - 20200889
ER -