Memristor Based Neuromorphic Network Security System Capable of Online Incremental Learning and Anomaly Detection

摘要

Real-time network intrusion and anomaly detection systems designed for battery powered devices are in high demand. This paper presents a study of unsupervised and supervised memristor based neuromorphic systems for such tasks. AutoEncoder (AE) and Multilayer Perceptron (MLP) algorithms are used to design memristor based intrusion and anomaly detection systems. The autoencoder shows strong intrusion detection performance with accuracy greater than 92.5% on zeroday attack packets. A real-time online incremental learning and anomaly detection system is also designed using the effective anomaly detection abilities of the AE. The learning system uses two autoencoders, one AE is pretrained for classifying network packets as normal and malicious, and the second AE is initialized with random weights and learns malicious data incrementally. Thus, this system is able to flag new attack classes during runtime. The real-time intrusion detection system performs with an accuracy greater than 89.7%. The memristor based implementation shows that the proposed system can be implemented using extreme low power for edge and IoT applications.