{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-2223"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-2223","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Analysis of signal resampling effects on attention-driven SEI for IoT systems","abstract":"The rapid growth of the Internet of Things (IoT) has connected billions of devices, many with minimal security, making them vulnerable to attacks. Specific Emitter Identification (SEI) offers a passive and reliable security solution by identifying devices through their unique hardware features, enabling serial number level distinction without altering the emitter. SEI can serve as the “something the entity is” factor in zero-trust multi-factor authentication frameworks. However, conventional SEI methods rely on high sampling rates, which are impractical for resource-limited IoT devices. This work evaluates an attention-based SEI model that maintains high identification accuracy at reduced sampling rates. The proposed approach achieves over 97% accuracy using only 2,500 signals sampled at 5 MHz and sustains above 90% accuracy under Rayleigh fading, reducing memory usage by 87.5% without compromising performance. These results highlight the potential of attention mechanisms for efficient, scalable IoT device identification.","abstract_html":"The rapid growth of the Internet of Things (IoT) has connected billions of devices, many with minimal security, making them vulnerable to attacks. Specific Emitter Identification (SEI) offers a passive and reliable security solution by identifying devices through their unique hardware features, enabling serial number level distinction without altering the emitter. SEI can serve as the “something the entity is” factor in zero-trust multi-factor authentication frameworks. However, conventional SEI methods rely on high sampling rates, which are impractical for resource-limited IoT devices. This work evaluates an attention-based SEI model that maintains high identification accuracy at reduced sampling rates. The proposed approach achieves over 97% accuracy using only 2,500 signals sampled at 5 MHz and sustains above 90% accuracy under Rayleigh fading, reducing memory usage by 87.5% without compromising performance. These results highlight the potential of attention mechanisms for efficient, scalable IoT device identification.","abstract_has_math":false,"creators":["Mohamedkhir, Mohamedelfateh"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Reising, Donald","Weerasena, Lakmali; Fadul, Mohamed K. M.","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2027,"date_issued":"2027-01-01T08:00:00Z","date_published":"2027-01-01T08:00:00Z","updated_at":"2026-07-24T05:47:28Z","subjects":["IEEE 802.11 (Standard)","Internet of Things--Computer networks--Security measures","Pattern recognition systems"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/1040","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Reising, Donald","Weerasena, Lakmali; Fadul, Mohamed K. 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S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."]},{"key":"dc:description.abstract","label":"Abstract","values":["The rapid growth of the Internet of Things (IoT) has connected billions of devices, many with minimal security, making them vulnerable to attacks. Specific Emitter Identification (SEI) offers a passive and reliable security solution by identifying devices through their unique hardware features, enabling serial number level distinction without altering the emitter. SEI can serve as the “something the entity is” factor in zero-trust multi-factor authentication frameworks. However, conventional SEI methods rely on high sampling rates, which are impractical for resource-limited IoT devices. This work evaluates an attention-based SEI model that maintains high identification accuracy at reduced sampling rates. The proposed approach achieves over 97% accuracy using only 2,500 signals sampled at 5 MHz and sustains above 90% accuracy under Rayleigh fading, reducing memory usage by 87.5% without compromising performance. These results highlight the potential of attention mechanisms for efficient, scalable IoT device identification."]},{"key":"dc:title","label":"Title","values":["Analysis of signal resampling effects on attention-driven SEI for IoT systems"]}]}],"canonical_facts":{"dc:contributor":["Reising, Donald","Weerasena, Lakmali; Fadul, Mohamed K. M.","College of Engineering and Computer Science"],"dc:creator":["Mohamedkhir, Mohamedelfateh"],"dc:date":["2025-12-01T08:00:00Z"],"dc:date.available":["2027-01-01T08:00:00Z"],"dc:description":["Dept. of Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."],"dc:description.abstract":["The rapid growth of the Internet of Things (IoT) has connected billions of devices, many with minimal security, making them vulnerable to attacks. Specific Emitter Identification (SEI) offers a passive and reliable security solution by identifying devices through their unique hardware features, enabling serial number level distinction without altering the emitter. SEI can serve as the “something the entity is” factor in zero-trust multi-factor authentication frameworks. However, conventional SEI methods rely on high sampling rates, which are impractical for resource-limited IoT devices. This work evaluates an attention-based SEI model that maintains high identification accuracy at reduced sampling rates. The proposed approach achieves over 97% accuracy using only 2,500 signals sampled at 5 MHz and sustains above 90% accuracy under Rayleigh fading, reducing memory usage by 87.5% without compromising performance. These results highlight the potential of attention mechanisms for efficient, scalable IoT device identification."],"dc:identifier":["https://scholar.utc.edu/theses/1040"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["IEEE 802.11 (Standard)","Internet of Things--Computer networks--Security measures","Pattern recognition systems"],"dc:title":["Analysis of signal resampling effects on attention-driven SEI for IoT systems"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:47:28Z"}