Neuromorphic Adaptive Resonance Theory for One-Shot Online Learning and Network Security

Title

Neuromorphic Adaptive Resonance Theory for One-Shot Online Learning and Network Security

Authors

Presenter(s)

Md Shahanur Alam

Files

Description

In this work, we present an one shot learning system capable of online learning for network intrusion detection. Adaptive resonance theory is implemented in custom low power memristor-based neuromorphic hardware. The system is capable of discriminating with existing knowledge to learn incrementally. To determine the winning neuron, the winner takes all circuit is implemented with CMOS and a capacitor. The timing of charging the winning capacitor was found in nanosecond range. The performance of the system was evaluated on both previously known and zero-day datasets. The detection accuracy using zero-day packets is 99.97%, and 99.99% for the known attacks. Furthermore, the system was tested using various vigilance parameters and learning rates. The variation of threshold voltage across the capacitor was also investigated to observe the effect on learning and detection accuracy.

Publication Date

4-22-2021

Project Designation

Graduate Research

Primary Advisor

Tarek M. Taha

Primary Advisor's Department

Electrical and Computer Engineering

Keywords

Stander Symposium Posters, School of Engineering

Neuromorphic Adaptive Resonance Theory for One-Shot Online Learning and Network Security

Share

COinS