Data-driven techniques for signal recovery and decryption

Wafaa Al Nassan; Talal Bonny; Mohammad Al-Shabi

doi:10.1117/12.3013887

7 June 2024 Data-driven techniques for signal recovery and decryption

Wafaa Al Nassan, Talal Bonny, Mohammad Al-Shabi

Proceedings Volume 13057, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII; 1305714 (2024) https://doi.org/10.1117/12.3013887
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

In an era where data security is paramount, encrypting sensitive information is ubiquitous. Traditional encryption methods often rely on complex algorithms and keys, making decryption an intricate and computationally intensive task. This paper introduces a novel approach to signal recovery and decryption, leveraging data-driven techniques, specifically Long Short-Term Memory (LSTM) neural networks. Our methodology is not limited to any specific encryption algorithm, allowing it to be applicable in various domains. We explore using LSTM networks as powerful tools for deciphering encrypted signals without prior knowledge of the encryption process. The critical insight behind our approach is the ability of LSTMs to capture intricate patterns and dependencies in the encrypted data, thus enabling the reconstruction of the original signal. We present experimental results that showcase the effectiveness of our data-driven decryption approach in a range of scenarios. This research signifies a paradigm shift in signal recovery and decryption, offering an alternative to traditional cryptanalysis techniques. By harnessing the power of data-driven modelling, we open new avenues for retrieving valuable information from encrypted signals, with potential applications in data cybersecurity and beyond.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wafaa Al Nassan, Talal Bonny, and Mohammad Al-Shabi "Data-driven techniques for signal recovery and decryption", Proc. SPIE 13057, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII, 1305714 (7 June 2024); https://doi.org/10.1117/12.3013887

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