{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/electro-magnetic-side-channel-attack-through","title":"Electro-Magnetic Side-Channel Attack Through Learned Denoising and Classification","arxiv_id":"1910.07201","date":"2019-10-16","proceeding":null,"authors":["Florian Lemarchand","Cyril Marlin","Florent Montreuil","Erwan Nogues","Maxime Pelcat"],"abstract":"This paper proposes an upgraded electro-magnetic side-channel attack that automatically reconstructs the intercepted data. A novel system is introduced, running in parallel with leakage signal interception and catching compromising data in real-time. Based on deep learning and character recognition the proposed system retrieves more than 57% of characters present in intercepted signals regardless of signal type: analog or digital. The approach is also extended to a protection system that triggers an alarm if the system is compromised, demonstrating a success rate over 95%. Based on software-defined radio and graphics processing unit architectures, this solution can be easily deployed onto existing information systems where information shall be kept secret.","url_abs":"https://arxiv.org/abs/1910.07201v1","url_pdf":"https://arxiv.org/pdf/1910.07201v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"electro-magnetic-side-channel-attack-through","repo_url":"https://github.com/opendenoising/interception_dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"electro-magnetic-side-channel-attack-through","repo_url":"https://github.com/opendenoising/extension","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[{"slug":"electro-magnetic-emanations-interception","name":"Electro-Magnetic Emanations Interception Dataset","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}