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IEEE International Conference on Communications workshops

ML-Based Advantage Distillation for Key Agreement in Underwater Acoustic Channels

Amedeo Giuliani, Francesco Ardizzon, Stefano Tomasin

Published May 28, 2023• 5 citations
Publisher

Abstract

Underwater networks have become an important topic of research due to their potential impact on several military and civil applications. To secure these communications, many protocols require the users to have symmetric secret keys. To distribute the keys, we can resort to physical layer key generation schemes, where the keys are generated by each user from the channel itself, by exploiting the environment as a source of randomness. We focus on the advantage distillation step of the secret key agreement (SKA) procedure, where the users extract the bit sequence from the channel observations. We propose an adversarial autoencoder (AAE) model for advantage distillation, which has multiple tasks: it enhances the reciprocity between legitimated channel features, decreases the reciprocity to the attacker channel features, and provides uniformly distributed bit sequences. The proposed approach is adaptive and directly derived from the training dataset, thus it does not require the a priori knowledge of the channel statistics. The solution is tested using channel observations collected from a sea experiment and compared to existing advantage distillation solutions, showing a significant improvement over the state of the art.

DOI
10.1109/iccworkshops57953.2023.10283635

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Underwater networks have become an important topic of research due to their potential impact on several military and civil applications. To secure these communications, many protocols require the users to have symmetric secret keys. To distribute the keys, we can resort to physical layer key generation schemes, where the keys are generated by each user from the channel itself, by exploiting the environment as a source of randomness. We focus on the advantage distillation step of the secret key agreement (SKA) procedure, where the users extract the bit sequence from the channel observations. We propose an adversarial autoencoder (AAE) model for advantage distillation, which has multiple tasks: it enhances the reciprocity between legitimated channel features, decreases the reciprocity to the attacker channel features, and provides uniformly distributed bit sequences. The proposed approach is adaptive and directly derived from the training dataset, thus it does not require the a priori knowledge of the channel statistics. The solution is tested using channel observations collected from a sea experiment and compared to existing advantage distillation solutions, showing a significant improvement over the state of the art.
Underwater acoustic communications (UWACs) are being adopted in several applications, and their security mechanisms require periodically renewed symmetric keys among legitimate devices. To this end, a secret key agreement (SKA) exploiting the UWAC context can be implemented. In this paper, we focus on one step of the source-based SKA protocol, i.e., the advantage distillation, when the legitimate users pre-process the channel measurement to increase the agreement while decreasing the attacker's knowledge about the key. We propose to learn the advantage distillation process by using a dataset of observed channels from the legitimate parties and the attacker, respectively. In turn, also the attacker uses such data to extract the key from its own underwater channel observations. The key extraction functions are derived through a computationally efficient data-driven adversarial strategy based on a metric related to the secret key rate that can be computed with a lower computational cost. The proposed strategy is tested considering experimental UWAC data, derived from a sea experiment. Results prove the quick convergence of the algorithm and the advantage of using the proposed techniques.
Many military and civil applications using underwater acoustic channels (UWACs) are paired with security services, providing, for instance, secrecy and authenticity to the communication.These mechanisms however often require periodically renewed symmetric keys among two communicating devices, namely Alice and Bob.To this end, secret keys can be agreed upon using the physicallayer communication channel, leveraging, in particular, the reciprocity and randomness of the UWAC to extract a common key that remains secret to an attacker (Eve).In this paper, we propose a novel solution for the advantagedistillation part of the secret key generation procedure.After channel probing, the advantage of distillation lets Alice and Bob extract a sequence of bits from their own (analog) measurements.We propose an asymmetric advantage-distillation protocol with two novel features: i) the quantizers used by Alice and Bob are derived maximizing the secret key capacity cooperating through a public channel and ii) Alice transmits a correction over a public authenticated side channel.Numerical results prove the effectiveness of our approach, showing that exchanging a few bits of information before information reconciliation allows extracting more secret bits from the channel.
Physical layer encryption is a promising solution to provide transmission security for Internet-of-Things (IoT). To realize physical layer encryption, channel state information (CSI) as a random source to generate secret keys should be exploited. The reciprocity of wireless channel between legitimate communication nodes which is typically assumed for time division duplex (TDD) systems, is of critical importance to ensure the consistency in generated keys at two nodes. However, in practical TDD systems, due to the asynchronous measurement of CSI, the receiver noise and the the difference in hardware, etc., the reciprocity of channel measurements between legitimate parties is hard to be guaranteed, which makes physical layer key generation approach infeasible. To address this issue, we propose a wireless key generation scheme based on deep learning techniques. Firstly, a feature extraction network, which consists of two auto-encoders trained jointly, is developed to extract the common feature from the different yet correlated channel observations at two legitimate nodes, which will be used as the source to generate keys. Then a quantizer with an adaptive guard band width is devised to output a binary secret-key sequence from the feature vector. Numerical results show that the proposed scheme achieves remarkable improvement in terms of key disagreement rate (KDR) and key generation rate (KGR) compared existing counterparts in the literature.
With the growing use of underwater acoustic communications and the recent adoption of standards in this field, it is becoming increasingly important to secure messages against eavesdroppers. In this paper, we focus on a physical-layer security solution to generate sequences of random bits (keys) between two devices (Alice and Bob) belonging to an underwater acoustic network (UWAN); the key must remain secret to a passive eavesdropper (Eve) not belonging to the UWAN. Our method is based on measuring the propagation delay of the underwater acoustic channel over multiple hops of the UWAN: this harvests the randomness in the UWAN topology and turns the slow sound propagation in water into an advantage against eavesdropping. Our key generation protocol includes a route discovery handshake, whereby all UWAN devices at intermediate hops accumulate their message processing delays. This enables Alice and Bob to compute the actual propagation delays along each route and to map such information to a sequence of bits. Finally, from these bit sequences, Alice and Bob obtain a secret key. We analyze the performance of the protocol theoretically and assess it via extensive simulations and field experiments.
Abstract The novel advantage distillation (AD) scheme for secret key agreement (SKA) scheme over relay communication system for the wiretap channel model is proposed. For the case where there are noiseless both the main channel and the eavesdropper’s channel, the SKA scheme for the noiseless relay communication (SKA-NRC) system is proposed. In this scheme, random sequences qA and qB are used as artificial interference to prevent eavesdropping. Simulation results show that with the proposed SKA schemes, the bit error ratio (BER) of eavesdropper Eve maintains stably at 0.5, while legitimate users Alice and Bob are able to get much lower BERs.
Physical Layer Secret Key Generation (PSKG) exploits wireless channel reciprocity to establish cryptographic keys between legitimate users, providing a promising solution for securing IoT networks. However, reciprocity in time-division duplex (TDD) systems is often compromised by hardware imperfections and channel noise, posing significant challenges for reliable key generation. To address these issues, this paper proposes an autoencoder-based noise augmentation (AE-NA) model that operates in an unsupervised manner. Specifically, the proposed model integrates dynamic Gaussian noise augmentation directly into the autoencoder architecture. This approach enhances feature extraction capabilities and improves noise robustness, enabling the network to learn noise-invariant representations effectively from noisy channel estimates. Furthermore, we present a comprehensive PSKG framework that incorporates the proposed AE-NA model. Reported results demonstrate that the AE-NA-based PSKG significantly reduces the key disagreement ratio (KDR) while enhancing key randomness, thereby providing a scalable and effective solution for secure key generation in dynamic IoT environments.