2024 BrainChip Discussion, page-5500

Could be the CyberNeuro-RT now live and available.

You'd like to think if selling it, that they will need to be sourcing the 2 "neuromorphic offerings" via direct eg BRN & Intel, via their own licence or via an existing licensee eg Megachips or Renesas?

Quantum Ventura

www.quantumventura.tech

CyberNeuro-RT

An AI/ML-driven, highly-scalable, real-time network defense & threat intelligence tool with GPUor low-power neuromorphic chip deployment

A Quantum Ventura, Lockheed Martin, and Penn State Innovation
Quantum Ventura’s CyberNeuro-RT (CNRT) technology offering has been developed in partnership with Lockheed Martin Co.’s MFC Division and Pennsylvania State University under partial funding from the U.S. Department of Energy.
Cutting-Edge Unsupervised ML

Scalable Unsupervised Outlier Detection (SUOD)

• Large-scale heterogeneous outlier detection
• 6 ML Algo EnsembleModel Approximation for Complex Models
• Execution Efficiency Improvement for Task Load Balancing in Distributed System
• Variational Autoencoder (VAE)

Variational Autoencoder (VAE)

• Encoder-Decoder Architecture
• Variational => Highly Regularized Encoder
• ETrained to Minimize Reconstruction Error of initial input and reconstructed output
• Variational Autoencoder (VAE)

75x Dataset Growth in Under 2 Months

1. Existing Dataset Ingestion: Proprietary system enables ingestion of any existing network capture dataset with flexible support for any labelling system
2. From-the-wild Zero Day Sampling: System enables capturing and simulation of novel threats for additional data sampling
3. Data Generation via Simulation: ThreatATI database and proprietary ingestion system enable sampling and augmentation for cataloged threats from proprietary and public threat databases

Proprietary Pipeline Adapts to Any Dataset

Follow Threats Home with Dark Web Tracking

At-the-edge Neuromorphic Processing
◯ Two offerings from the leading neuromorphic developers: Intel and Brainchip
◯ Small form factor, magnitudes less power consumption than GPU
◯ On-chip learning for deployment network specific attack detection

Intel Loihi

Brainchip Akida

Dashboards Minimizes Operator Fatigue
Robust,Multi-Faceted, User-FriendlyCyber Analyst DashboardOperator FatigueAllows Cyber Attacks To Happen

• Large numbers of false alarms cause real threats to be missed
• False alarms fatigue the cyber analyst further increasing risk of missed threats

‍
The Cyber Neuro-RT Dashboard Is Designed ToMinimize All Sources Of Analyst FatigueWhile PresentingTimely And Meaningful Data Insights

• Albased false alarms are minimized (trained for minimal false positive rate)n for cataloged threats from proprietary and public threat databases
• Possible threats are ranked by importance and confidence
• Only the most relevant and likely alarms are actioned upon

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