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Red Teaming Generative AI/NLP, the BB84 quantum cryptography protocol and the NIST-approved Quantum-Resistant Cryptographic Algorithms
In the contemporary digital age, Quantum Computing and Artificial Intelligence (AI) convergence is reshaping the cyber landscape, introducing unprecedented opportunities and potential vulnerabilities. This research, conducted over five years, delves into the cybersecurity implications of this convergence, with a particular focus on AI/Natural Language Processing (NLP) models and quantum cryptographic protocols, notably the BB84 method and specific NIST-approved algorithms.
Disease X vaccine production and supply chains: risk assessing healthcare systems operating with artificial intelligence and industry 4.0
A set of six algorithmic solutions is presented for resolving vaccine production and supply chain bottlenecks.
Super forecasting the technological singularity risks from artificial intelligence
The article forecasts emerging cyber-risks from the integration of AI in cybersecurity.
Review of the state of the art in autonomous artificial intelligence
The article integrates and consolidates the findings from existing literature and advances the AutoAI design into (1) using new and emerging sources of data for teaching and training AI algorithms and (2) enabling AI algorithms to use automated tools for training new and improved algorithms.
Advancing the cybersecurity of the healthcare system with self-optimising and self-adaptative artificial intelligence (part 2)
This article advances the knowledge on teaching and training new artificial intelligence algorithms, for securing, preparing, and adapting the healthcare system to cope with future pandemics.
Design of a dynamic and self adapting system, supported with artificial intelligence, machine learning and real time intelligence for predictive cyber risk analytics in extreme environments, cyber risk in the colonisation of Mars
This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real time intelligence in edge computing.
