An algebraic expert system with neural network concepts for cyber, big data and data migration

This paper describes a machine assistance approach to grading decisions for values that might be missing or need validation, using a mathematical algebraic form of an Expert System, instead of the traditional textual or logic forms and builds a neural network computational graph structure. This Experts System approach is also structured into a neural network like format of: input, hidden and output layers that provide a structured approach to the knowledge-base organization, this provides a useful abstraction for reuse for data migration applications in big data, Cyber and relational databases. The approach is further enhanced with a Bayesian probability tree approach to grade the confidences of value probabilities, instead of the traditional grading of the rule probabilities, and estimates the most probable value in light of all evidence presented. This is ground work for a Machine Learning (ML) experts system approach in a form that is closer to a Neural Network node structure

Artificial intelligence methods for security and cyber security systems

This research is in threat analysis and countermeasures employing Artificial Intelligence (AI) methods within the civilian domain, where safety and mission-critical aspects are essential. AI has challenges of repeatable determinism and decision explanation. This research proposed methods for dense and convolutional networks that provided repeatable determinism. In dense networks, the proposed alternative method had an equal performance with more structured learnt weights. The proposed method also had earlier learning and higher accuracy in the Convolutional networks. When demonstrated in colour image classification, the accuracy improved in the first epoch to 67%, from 29% in the existing scheme. Examined in transferred learning with the Fast Sign Gradient Method (FSGM) as an analytical method to control distortion of dissimilarity, a finding was that the proposed method had more significant retention of the learnt model, with 31% accuracy instead of 9%. The research also proposed a threat analysis method with set-mappings and first principle analytical steps applied to a Symbolic AI method using an algebraic expert system with virtualized neurons. The neural expert system method demonstrated the infilling of parameters by calculating beamwidths with variations in the uncertainty of the antenna type. When combined with a proposed formula extraction method, it provides the potential for machine learning of new rules as a Neuro-Symbolic AI method. The proposed method uses extra weights allocated to neuron input value ranges as activation strengths. The method simplifies the learnt representation reducing model depth, thus with less significant dropout potential. Finally, an image classification method for emitter identification is proposed with a synthetic dataset generation method and shows the accurate identification between fourteen radar emission modes with high ambiguity between them (and achieved 99.8% accuracy). That method would be a mechanism to recognize non-threat civil radars aimed at threat alert when deviations from those civilian emitters are detected