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

This version is not peer-reviewed.

Submitted:

27 January 2025

Posted:

28 January 2025

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Abstract
An ultra-low-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—one 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—774× faster than the ASUS Tinkerboard edge processor—and delivers an energy efficiency of 783 GOPS/W, consuming only 20.5 mW during anomaly detection.
Keywords: 
Online Learning; Memristor; Neuromorphic; On-chip; Unsupervised; Autoencoder; Anomaly Detection; Intrusion Detection
Subject: 
Engineering  -   Electrical and Electronic Engineering
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.

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