2024 BrainChip Discussion, page-8995

This article just came up in the CSIAC sit from 7 hours ago on CyberNeuro-RT project with Quantum Ventura.

Can't post all as too large.

ABOUT CSIAC

The Cybersecurity and Information Systems Information Analysis Center (CSIAC) is a component of the U.S. Department of Defense’s (DoD's) Information Analysis Center (IAC) enterprise, serving the defense enterprise of DoD and federal government users and their supporting academia and industry partners.

https://csiac.org/articles/low-power-cybersecurity-attack-detection-using-deep-learning-on-neuromorphic-technologies/


Low-Power Cybersecurity Attack Detection Using Deep Learning on Neuromorphic Technologies

Home / Articles / Journals / CSIAC 2024: Volume 8 Issue 2

Summary

Neuromorphic computing systems are desirable for several applications because they achieve similar accuracy to graphic processing unit (GPU)-based systems while consuming a fraction of the size, weight, power, and cost (SWaP-C). Because of this, the feasibility of developing a real-time cybersecurity system for high-performance computing (HPC) environments using full precision/GPU and reduced precision/neuromorphic technologies was previously investigated [1]. This work was the first to compare the performance of full precision and neuromorphic computing on the same data and neural network and Intel and BrainChip neuromorphic offerings. Results were promising, with up to 93.7% accuracy in multiclass classification—eight attack types and one benign class.

Since then, a BrainChip Akida 1000 chip was acquired, and Intel released the Loihi 2 chip and developed the Lava framework for establishing neuromorphic deep-learning applications. These developments and more detailed analyses are reflected in this article, with up to 98.4% accuracy achieved in classifying nine classes. Compared to the state of the art, neuromorphic technologies have much smaller SWaP-C profiles. In addition, how these systems can be applied to deployable platforms like manned aircraft or unmanned aerial vehicles (UAVs) is discussed, and additional use cases of neuromorphic computing in computer vision are reviewed.