Securing National Critical Infrastructure With Adaptive Machine Learning | Dark Star Quantum Lab
Channel: Dark Star Quantum Lab
Date: 2021-03-15

#darkstar #quantumcomputing  #MESA
Securing National Critical Infrastructure With Adaptive Machine Learning 
ABSTRACT
Managing outside attacks on internal data is of fundamental important to organizations. Including Cyber- Physical System (CPS), which is a complex mixture of physical and computer components typically monitored or controlled by computer-based algorithms. However, there has been an urgent need to safeguard insider’s behavior of breaching the expected code of conduct in maintaining the critical organizations’ data and assets.

To this end, analysis of the brainwave signals will be performed using an advanced deep learning algorithm called Long Short Term Memory Recurrent Neural Network (LSTM- RNN) classifier which will remember a previous mental state of each insider and compare it with new present brain state to classify the risk level associated.

The brain wave is also analysed with Adaptive Machine Learning Algorithm which combines several weak learners to form a single strong learner. In this study, our target is to increase the security of National Critical Infrastructure by providing a significant proof of concept to detect insider threats through fitness evaluation using EEG signals that get analyzed using deep learning algorithms to classify different mental states into four categories risk matrix.

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Dark Star Quantum Lab is a quantum information technology hardware and software solution provider for civilian, defense & space domains.
MISSION / VISION
1. Enable the circular economy (circularity) value-chain for the quantum eco-system;
2. Enable the transition from classical to quantum technologies, filling in the blanks.
3. Make investment in the quantum eco-system user friendly.  
We have expertise to identify "winning horses" of quantum investment opportunities.
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