Orthogonal blinding based schemes for wireless physical
layer security aim to achieve secure communication by
injecting noise into channels orthogonal to the main channel and
corrupting the eavesdropper’s signal reception. These methods,
albeit practical, have been proven vulnerable against multiantenna
eavesdroppers who can filter the message from the noise.
The venerability is rooted in the fact that the main channel state
remains stasis in spite of the noise injection, which allows an
eavesdropper to estimate it promptly via known symbols and filter
out the noise. Our proposed scheme leverages a reconfigurable
antenna for Alice to rapidly change the channel state during
transmission and a compressive sensing based algorithm for her
to predict and cancel the changing effects for Bob. As a result,
the communication between Alice and Bob remains clear, whereas
randomized channel state prevents Eve from launching the knownplaintext
attack. We formally analyze the security of the scheme
against both single and multi-antenna eavesdroppers and identify
its unique anti-eavesdropping properties due to the artificially
created fast changing channel. We conduct extensive simulations
and real-world experiments to evaluate its performance. Empirical
results show that our scheme can suppress Eve’s attack success
rate to the level of random guessing, even if she knows all the
symbols transmitted through other antenna modes.
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Adversarial Filters for Secure Modulation Classification
Modulation Classification (MC) is the problem of classifying the modulation format of a wireless signal. In the wireless communications pipeline, MC is the first operation performed on the received signal and is critical for reliable decoding. This paper considers the problem of secure MC, where a transmitter (Alice) wants to maximize MC accuracy at a legitimate receiver (Bob) while minimizing MC accuracy at an eavesdropper (Eve). This work introduces novel adversarial learning techniques for secure MC. We present adversarial filters in which Alice uses a carefully designed adversarial filter to mask the transmitted signal, that can maximize MC accuracy at Bob while minimizing MC accuracy at Eve. We present two filtering-based algorithms, namely gradient ascent filter (GAF), and a fast gradient filter method (FGFM), with varying levels of complexity. Our proposed adversarial filtering-based approaches significantly outperform additive adversarial perturbations (used in the traditional machine learning (ML) community and other prior works on secure MC) and have several other desirable properties. In particular, GAF and FGFM algorithms are a) computational efficient (allow fast decoding at Bob), b) power-efficient (do not require excessive transmit power at Alice); and c) SNR efficient (i.e., perform well even at low SNR values at Bob).
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- NSF-PAR ID:
- 10312264
- Date Published:
- Journal Name:
- Asilomar Conference on Signals, Systems, and Computers
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
