TLDR: This paper introduces Security Semantic ISAC (SS-ISAC), a framework that uses pluggable AI-powered encryption and decryption modules (Adversarial Residual Networks) to secure semantic-driven integrated sensing and communication (ISAC) systems against eavesdropping. It generates adversarial attacks to fool eavesdroppers while mitigating them for legitimate users, achieving strong security and maintaining sensing/communication performance without requiring a complete system redesign.
Integrated Sensing and Communication (ISAC) is a groundbreaking technology poised to transform future wireless systems, from railways to vehicular networks and unmanned aerial vehicles. It efficiently combines sensing and communication functionalities, leading to improved spectral and energy efficiency. Alongside this, semantic communication, which focuses on transmitting the underlying meaning of messages rather than raw bits, offers immense potential for resource optimization in wireless systems.
However, the integration of deep neural networks (DNNs) in semantic-driven ISAC systems, while enhancing performance, also introduces vulnerabilities. The broadcast nature of wireless channels makes these systems susceptible to eavesdropping threats. Current approaches to bolster security often demand a complete redesign or retraining of the entire semantic-driven ISAC system, incurring substantial computational costs and inflexibility.
Addressing this critical challenge, a new framework called Security-aware Semantic-driven ISAC (SS-ISAC) has been proposed. This innovative framework introduces a flexible and security-conscious approach to protect sensitive information without overhauling existing infrastructure. The core of SS-ISAC lies in its unique “paired pluggable encryption and decryption modules.”
These modules are designed to be easily integrated or removed based on security needs. The encryption module is strategically placed after the semantic transmitter. Inspired by the concept of adversarial attacks in machine learning, it employs a trainable Adversarial Residual Network (ARN) to generate a subtle “adversarial attack.” This attack is specifically crafted to confuse potential eavesdroppers, making it difficult for them to correctly interpret the transmitted information.
Conversely, a corresponding decryption module is situated before the semantic receiver. This module also utilizes a trainable ARN, but its role is to mitigate the adversarial attack and any noise present in the signal. This ensures that legitimate receivers can accurately recover the original semantic information for both sensing and communication tasks.
A significant advantage of SS-ISAC is its joint optimization strategy. The ARNs within both the encryption and decryption modules are trained together. This optimization process minimizes a carefully designed loss function that considers several crucial factors: the power of the adversarial attack, the performance of sensing and communication (SAC) tasks, and the risk of privacy leakage to eavesdroppers. This balanced approach ensures robust security without compromising the primary functions of the ISAC system.
The effectiveness of the proposed SS-ISAC framework has been validated through extensive simulations. Results demonstrate that SS-ISAC achieves superior performance in both SAC tasks and eavesdropping prevention. For instance, under various signal-to-noise ratio (SNR) conditions, the framework significantly reduces the probability of an eavesdropper correctly classifying information—by approximately 50% at 20 dB SNR—while maintaining high-quality sensing and communication. Furthermore, its performance remains strong across different perturbation-to-signal ratios, highlighting its robust attack capacity.
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This research offers a promising path forward for securing future wireless systems, providing a flexible and efficient solution to protect semantic-driven ISAC communications from unauthorized access. You can find more details about this innovative framework in the full research paper: Security-aware Semantic-driven ISAC via Paired Adversarial Residual Networks.
