skip to main content
10.1145/3468218.3469043acmconferencesArticle/Chapter ViewAbstractPublication PageswisecConference Proceedingsconference-collections
short-paper

Adversarial Learning for Cross Layer Security

Published: 28 June 2021 Publication History

Abstract

Spectrum access in the next generation wireless networks will be congested, competitive, and vulnerable to malicious intents of strong adversaries. This compels us to rethink wireless security for a cross-layer solution addressing it as a joint problem for encryption and modulation. We propose a novel neural network generated cross-layer security algorithm where the trusted transmitter encodes a secret message using a shared secret key to generate a secured waveform. This encrypted waveform remains undeciphered by the adversary while the intended receiver can recover the secret. Cooperative learning is introduced to enable our trusted pair to defeat the adversary and learn the encryption and modulation jointly. The model can encode any modulation order and improves both reliability and secrecy capacity compared to prior work. Our results demonstrate that the trusted pair succeeds in achieving secure data transmission while the adversary can not decipher the received cipher data.

References

[1]
Martín Abadi and David G Andersen. 2016. Learning to protect communications with adversarial neural cryptography. arXiv preprint arXiv:1610.06918 (2016).
[2]
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: A system for large-scale machine learning. In 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16).
[3]
Karl-Ludwig Besser, Pin-Hsun Lin, Carsten R Janda, and Eduard A Jorswieck. 2019. Wiretap code design by neural network autoencoders. IEEE Transactions on Information Forensics and Security 15 (2019), 3374--3386.
[4]
Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. In Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation. Association for Computational Linguistics, Doha, Qatar, 103--111. https://doi.org/10.3115/v1/W14-4012
[5]
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).
[6]
Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, and Stefano Ermon. 2019. Neural joint source-channel coding. In International Conference on Machine Learning. PMLR, 1182--1192.
[7]
IEEE Computer Society LAN/MAN Standards Committee et al. 2007. IEEE Standard for Information technology-Telecommunications and information exchange between systems-Local and metropolitan area networks-Specific requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. IEEE Std 802.11 (2007).
[8]
Murilo Coutinho, Robson de Oliveira Albuquerque, Fábio Borges, Luis García Villalba, and Tai-Hoon Kim. 2018. Learning perfectly secure cryptography to protect communications with adversarial neural cryptography. Sensors 18, 5 (2018), 1306.
[9]
Thomas M Cover. 1999. Elements of information theory. John Wiley & Sons.
[10]
Rick Fritschek, Rafael F Schaefer, and Gerhard Wunder. 2019. Deep learning for the Gaussian wiretap channel. In ICC 2019-2019 IEEE International Conference on Communications (ICC). IEEE, 1--6.
[11]
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep learning. MIT press.
[12]
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Advances in Neural Information Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger (Eds.). 2672--2680.
[13]
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014).
[14]
Alex Graves and Jürgen Schmidhuber. 2005. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural networks 18, 5-6 (2005), 602--610.
[15]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Comput. 9, 8 (Nov. 1997), 1735--1780. https://doi.org/10.1162/neco.1997.9.8.1735
[16]
Hyeji Kim, Yihan Jiang, Ranvir Rana, Sreeram Kannan, Sewoong Oh, and Pramod Viswanath. 2018. Communication algorithms via deep learning. arXiv preprint arXiv:1805.09317 (2018).
[17]
Thomas Marchioro, Nicola Laurenti, and Deniz Gündüz. 2020. Adversarial Networks for Secure Wireless Communications. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE.
[18]
Hesham Mohammed and Dola Saha. 2020. Learning Secured Modulation With Deep Adversarial Neural Networks. In 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). 1--7. https://doi.org/10.1109/VTC2020-Fall49728.2020.9348833
[19]
Timothy O'Shea and Jakob Hoydis. 2017. An introduction to deep learning for the physical layer. IEEE Transactions on Cognitive Communications and Networking 3, 4 (2017), 563--575.
[20]
Maithra Raghu and Eric Schmidt. 2020. A Survey of Deep Learning for Scientific Discovery. arXiv:2003.11755 [cs.LG]
[21]
Frank Rubin. 1996. One-time pad cryptography. Cryptologia 20, 4 (1996), 359--364.
[22]
Claude Elwood Shannon. 1948. A mathematical theory of communication. Bell system technical journal 27, 3 (1948), 379--423.
[23]
Aaron D Wyner. 1975. The wire-tap channel. Bell system technical journal 54, 8 (1975), 1355--1387.
[24]
Zhongyuan Zhao, Mehmet C. Vuran, Fujuan Guo, and Stephen D. Scott. 2021. Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks. arXiv:1810.07181 [eess.SP]

Cited By

View all

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
WiseML '21: Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning
June 2021
104 pages
ISBN:9781450385619
DOI:10.1145/3468218
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2021

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Adversarial Learning
  2. End-to-end Encryption
  3. Information Theoretic Analysis
  4. Wireless Security

Qualifiers

  • Short-paper
  • Research
  • Refereed limited

Conference

WiSec '21

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 113
    Total Downloads
  • Downloads (Last 12 months)7
  • Downloads (Last 6 weeks)0
Reflects downloads up to 13 Feb 2025

Other Metrics

Citations

Cited By

View all

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media