Neuromorphic Adaptive Resonance Theory for One-Shot Online Learning and Network Security
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 project, School of Engineering
Recommended Citation
"Neuromorphic Adaptive Resonance Theory for One-Shot Online Learning and Network Security" (2021). Stander Symposium Projects. 2368.
https://ecommons.udayton.edu/stander_posters/2368
