Threats of Deep Learning Attacks to Chaos-Based Spread-Spectrum System
Abstract
Chaos-based spread-spectrum systems exploit chaotic carriers or sequences resembling random noise to transmit and recover information, often employing chaotic spectrum spreading as a form of physical-layer encryption. Representative spread-spectrum schemes, such as Chaotic CDMA and Differential Chaos Shift Keying (DCSK), have been shown to be highly sensitive to noise, parameter mismatches, and their reliance on obfuscation makes them vulnerable to sophisticated attacks. In this paper, we examine the susceptibility of chaos-based spread-spectrum schemes to modern deep learning techniques. By integrating Gramian Angular Field (GAF) encoding with the ResNet50 architecture, we demonstrate effective prediction and reconstruction of hidden chaotic key sequences, even under noisy and fading channel conditions. Experimental results confirm that digital communication systems employing chaotic spread-spectrum modulation can be systematically compromised by advanced deep learning methods, posing new challenges for enhancing their robustness and cryptographic reliability in practical applications.
Published
2026-05-13
Issue
Section
REV-ECIT Special Issue
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