Detection of TEMPEST audio compromising signal using artificial intelligence
Обнаружение компрометирующего TEMPEST-аудиосигнала с использованием искусственного интеллекта
2024-10-03
SCID: 54.1/hpzt2x8w
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TEMPESTartificial intelligenceaudio compromising signalaudio signal analysismachine learning
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Abstract (AI)
In the contemporary landscape of cybersecurity, electronic devices are increasingly susceptible to various forms of exploitation, with TEMPEST (Transient Electromagnetic Pulse Emanation Standard) attacks posing a significant threat. This article delves into the application of artificial intelligence (AI) for detecting vulnerabilities in electronic devices through audio signal analysis within the TEMPEST domain. By leveraging advanced AI techniques, particularly machine learning algorithms, this study identifies a novel approach regarding the TEMPEST evaluation of electronic devices which process audio signal using artificial intelligence. The process involves the collection and processing of audio signals emanating from electronic devices, followed by the application of AI models to detect the presence of audio information in the received signal. The findings underscore the efficacy of AI involved in TEMPEST evaluation of electronic devices.
Key Findings
1
AI-based machine learning models can detect audio information in signals emanating from electronic devices relevant to TEMPEST attacks.
2
Results indicate that AI is an effective tool for TEMPEST evaluation and detecting audio-based vulnerabilities in electronic equipment.
3
The methodology involves collecting and processing emanated audio signals from devices before applying AI models to identify compromising audio content.
4
The study presents a novel AI-driven approach for TEMPEST evaluation of electronic devices that process audio signals.
Research Object
Audio signals emanating from electronic devices vulnerable to TEMPEST (electromagnetic/audio compromising emissions)
Research Subject
Detection of audio compromising TEMPEST signals using artificial intelligence / machine learning to identify presence of audio information in received emissions for TEMPEST evaluation
Publication Details
Publication Date
2024-10-03
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