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:
Development
● General Development - XTools, Swift, C & C++, Python, Jupyter
● High Performance Parallelism and Multi-processing - Python
● High Speed Demand (Assembler) - NASM / YASM
Research
● 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:
[ SPECIALIST TECHNICAL ARENA/MATERIAL REDACTED ]
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)
NNGL
Short Peer Review and Preparation(2023)
DMAD&R
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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