The CDD R&D arena includes:
Full Integration of 114 Cores & 960GB RAM / 20TB Storage / 40GBit N/W / Combined 31.8GHz - ZMM-[512bit] + Xeon throughput providing advanced, ultra-high performance GP-GPU (processing across large matrices (tot. 33GB) with + Metal-3 and support augmented CUDA) combined on:
- AMD Radeon Pro 5500M - Intel UHD Graphics 630 - AMD Radeon ProVega 64X
High Throughput Machine Space:
Apple Servers: MacPro-128GB, iMacPro-128GB and MacMini-64GB
Development and Analytics:
Apple MacBook Pro & iMacPro
Design and Research:
iMac and iPad Pro
[ SPECIALIST TECHNICAL ARENA/MATERIAL REDACTED ]
All Technical Documentation - LaTeX
Innovation - Brain Tech - Notability - ProCreate
Development - SWIFT Pycharm
Network Analytics - WireShark
CDD has progressed development in a number of areas of cyber security which call upon techniques from expert systems, neural networks, machine learning, deep neural networks and augmented human cognition (that some confuse with AI).
A modest portfolio of sub-systems are available to consultants in the performance of specialist security services.
Some organisations have adopted similar techniques in bespoke engineering scenarios.
CAVA - Cyber Audit and Verification Arena
CORE - Cyber Operational Response Engineering
CSA - CyberSec Situational Awareness
SAM - Security Assurance Metrication
DSA - Dynamic Security Assessment
SIMS - Security Information Mapping System
WHISKAS - Worldwide Holistic Intelligence Surveillance and Knowledge Architecture for Security
OILS - Operational Intelligence and Logistics Systems [Tactial - (TOILS) & Strategic (SOILS)]
SMACC - Security Management Automation for Command and Control
MANTIS - A micro-segmentation approach for neutralisation of targeted information systems.
CCAS - Cyber Conflict Analytics System
ARTTS - Advanced Response Techniques to Threatened Systems
VIPPA - Vector Integration, Process and Protocol Analytics
VASE - Vulnerability Analytics Search Engine
CIA - Consequential Impact Analytics
CsMM - Cybersecurity Maturity Methodology
LICKS - Logical Intelligence Control for Knowledge-based System(s).
TrEAD - Trust Engineering, Architecture and Deployment
CLEAR - Correlated Log Engineering for Advanced Reconnaissance.
ACTOR - Advanced Cryto-key Transmission for Operational Resilience
FRBSA - An advanced Facial Recognition Biometric Security Architecture for dynamic identity and access management based upon SysPlex data management. No more passwords!
SqP/SyP/SmP Data Structure
Final Preparation for Submission (2024)
Short Peer Review and Preparation(2023)
In Preparation for Submission (2024)
A question is repeatedly arising for CDD, namely how firms and individuals might get involed with machine learning architectures.
A really good start has been provided by a Dr. Jason Browlee...a very experienced proponent of machine learning (and AI if you like such terminology), and this document provides some excellent guidance on how to approach the subject. There is a very large corpus of material in the CDD technical library and this can be made available to anyone making a reasonable request and whom might benefit from such access. (Please make contact).
How CDD goes about preparing high-throughput hyper-parallel processing architectures using GPUs.
Details the instruction set and the microcode formats native to this family of processors that are accessible to programmers and compilers. The main purposes of this document are to specify the language constructs and behavior, including the organization of each type of instruction in both text syntax and binary format. Also it provide a reference of instruction operation that compiler writers can use to maximize performance of the general purpose AMD processor.
NAT Interim Incident Report
Learning to learn with Quantum Neural Networks via classical neural networks
Finding good parametrisation characteristics for QNN, rapidly identifying approximate optima in the parametr landscape (QAOA for MaxCut and QAOA for Sherrington-Kirkpatrick Ising model and Variational Quantum Eignesolver for the Hubbard model), reduction in the number of optimization iterations, optimization strategies learned by the NN generalsing well across a number of problem instance sizes and training on small problem instances in oder to initialise larger, intractable simulations on quantum devices. LINK
Universal quantum control through deep reinforcement learning
Control optimization of reinforcement learning techniques using DNN.
TensorFlow Quantum: A software framework for quantum machine learning
The introduction of TFQ for high level abstractions for the design and training of both generative and discriminitive quantum infomration models for simulation of high performance quantum circuits.
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