CDD Research & Development 

Research Topics Facilities

● Advanced machine augmented learning architectures - SqP/SyP
● Applied Machine Learning - Hybrid Cybersecurity (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

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

● High Speed Demand (Assembler)  -  NASM / YASM


● Augmented Human Congition ('Generative AI') -  KERAS, TensorFlow,  PYTorch, Scikit-Learn,  MATLAB

● Quantum Computation -  CIRQ, IBM Qiskit, TensorFlow-Quantum

CDD Operational Environ  (dedicated entirely to support our research)

Combinatorial/Operational Summary: 

180 Cores / 0.75TB RAM / 16TB Storage / 40GBit N/W   (31.8GHz - ZMM-[512bit] + Xeon throughput)

High Throughput Machine Space: 

Apple Servers - MacPro and MacMini

Development and Analytics

Apple MacBook Pro & iMacPro

Design and Research

iMac and iPad Pro

Software Arena


All Technical Documentation - LaTeX

Innovation - Brain Tech

Development - SWIFT

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

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

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)

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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