Cyber-Neuro RT: Real-time Neuromorphic Cybersecurity

Wyler Zahm, Tyler Stern, Malyaban Bal, Abhronil Sengupta, Aswin Jose, Suhas Chelian, Srini Vasan

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


High throughput environments, such as those found in high performance computing (HPC) clusters, run at substantially greater scales than standard business IT domains. As a result, cybersecurity tools built for businesses utilizing standard machine learning frameworks are unable to handle the increased amount of traffic and connections in high throughput environments. Neural networks, in combination with edge-based next-generation embedded technologies such as neuromorphic processors, offer a solution to cybersecurity challenges in high throughput environments. Deep learning (DL), deep learning to spiking neural network (DL-to-SNN) conversions, and design exploration inside SNNs were among the experiments employed to explore this area. Neuromorphic implementations of deep learning networks often provide the same accuracy as full precision models while saving substantial power and cost. We explore this statement in the cybersecurity domain. Results are promising but will be further investigated.

Original languageEnglish (US)
Pages (from-to)536-545
Number of pages10
JournalProcedia Computer Science
Issue numberC
StatePublished - 2022
Event13th Annual Meeting of the BICA Society: 2022 Annual International Conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence, BICA*AI 2022 - Guadalajara, Mexico
Duration: Sep 22 2022Sep 25 2022

All Science Journal Classification (ASJC) codes

  • General Computer Science


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