Memristor-Based Neuromorphic System for Unsupervised Online Learning and Network Anomaly Detection on Edge Devices

Memristor-Based Neuromorphic System for Unsupervised Online Learning and Network Anomaly Detection on Edge Devices

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 dat...

Full description

Saved in:
Bibliographic Details
Main Authors: Md Shahanur Alam, Chris Yakopcic, Raqibul Hasan, Tarek M. Taha
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Information
Subjects:
online learning
memristor
neuromorphic
on-chip
unsupervised
autoencoder
Online Access:https://www.mdpi.com/2078-2489/16/3/222
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items