CDD Research & Development

Research Topics, Methodologies & Facilities

● Advanced machine augmented learning architectures - SqP/SyP
● Applied Machine Learning - Hybrid cybersecurity for blended attacks 
●Augmented Identification and Authentication - Similarity Analytics 
● Alternative Quantum data structures supportive of Quantum Machine Learning (QML)
● Enhanced, blended cybersecurity modeling using formal frameworks 
(E.g. NIST CSF  -  ISO27K  -  NCSC-CAF)
● Commercial Quantum Readiness Programme
● Methodology Interoperation (e.g.CDD[CMM]/NIST-CSF/MITREAtt&ck)
● Strict and full PRINCE-II Development Disciplines (Internal to CDD)

See Recent Reading below:

The CDD R&D arena includes:


  • General Development  -  XTools,  Swift,  C & C++,  Python,  Jupyter
  • High Performance Parallelism and Multi-processing -  Python / OpenCL
  • High Speed Demand (Assembler)  -  NASM / YASM / CUDA


  • Augmented Human Congition ('Generative AI') -  KERAS, TensorFlow,  PYTorch, Scikit-Learn,  MATLAB,  Numpy,  (+ Bitarray etc.) 
  • Quantum Computation -  CIRQ, IBM Qiskit, TensorFlow-Quantum

CDD Operational Environ  (dedicated entirely to supporting our research)

Combinatorial/Operational Summary: 

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

Software Arena


All Technical Documentation - LaTeX

Innovation - Brain Tech - Notability - ProCreate

Development - SWIFT Pycharm

Network Analytics - WireShark

System Development

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.  

CDD Cyber Assessment

CAVA - Cyber Audit and Verification Arena

CORE - Cyber Operational Response Engineering

CSA - CyberSec Situational Awareness

CDD Assurance

SAM - Security Assurance Metrication

DSA - Dynamic Security Assessment

SIMS - Security Information Mapping System

CDD Intelligence Services 

WHISKAS - Worldwide  Holistic Intelligence Surveillance and Knowledge Architecture for Security

OILS - Operational Intelligence and Logistics Systems [Tactial - (TOILS) & Strategic (SOILS)]

CDD Cyber Operations

SMACC - Security Management Automation for Command and Control

MANTIS - A micro-segmentation approach for neutralisation of targeted information systems.

CCAS - Cyber Conflict Analytics System

CDD Risk Management

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).

CDD Trust Architectures

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! 

CDD Patent Preparation/Submissions

SqP/SyP/SmP Data Structure

Final Preparation for Submission (2024)


Short Peer Review and Preparation(2023) 


In Preparation for Submission (2024)

CDD Recent Reading (for research guidance and commentary)

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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