{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T15:42:12Z","timestamp":1769010132439,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T00:00:00Z","timestamp":1741824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1718633"],"award-info":[{"award-number":["1718633"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>An ultralow-power, high-performance online-learning and anomaly-detection system has been developed for edge security applications. Designed to support personalized learning without relying on cloud data processing, the system employs sample-wise learning, eliminating the need for storing entire datasets for training. Built using memristor-based analog neuromorphic and in-memory computing techniques, the system integrates two unsupervised autoencoder neural networks\u2014one utilizing optimized crossbar weights and the other performing real-time learning to detect novel intrusions. Threshold optimization and anomaly detection are achieved through a fully analog Euclidean Distance (ED) computation circuit, eliminating the need for floating-point processing units. The system demonstrates 87% anomaly-detection accuracy; achieves a performance of 16.1 GOPS\u2014774\u00d7 faster than the ASUS Tinker Board edge processor; and delivers an energy efficiency of 783 GOPS\/W, consuming only 20.5 mW during anomaly detection.<\/jats:p>","DOI":"10.3390\/info16030222","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T12:06:59Z","timestamp":1741867619000},"page":"222","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Memristor-Based Neuromorphic System for Unsupervised Online Learning and Network Anomaly Detection on Edge Devices"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5166-1709","authenticated-orcid":false,"given":"Md Shahanur","family":"Alam","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Dayton, Dayton, OH 45469, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6401-272X","authenticated-orcid":false,"given":"Chris","family":"Yakopcic","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Dayton, Dayton, OH 45469, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raqibul","family":"Hasan","sequence":"additional","affiliation":[{"name":"Center for Computational and Data Sciences, Independent University, Dhaka 1229, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tarek M.","family":"Taha","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Dayton, Dayton, OH 45469, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.iotcps.2023.02.004","article-title":"Edge AI: A survey","volume":"3","author":"Singh","year":"2023","journal-title":"Internet Things Cyber-Phys. 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