As for Brainchip connections to NATO they have as most are...

As for Brainchip connections to NATO they have as most are aware purchased a licence to a spiking neural network cybersecurity algorithm developed by Professor Iliadis and Dr. Demertzis.

Interestingly Dr. Demertzis as you will see from the below extract from his CV strong and continuing links to the Greek Defence Force and although not contained in this extract in his LinkedIn profile which is not opening as it once did he was the Greek Defence Liaison for cybersecurity issues to NATO.

The paper which I have included is one of their latest joint works in this area and you can see from the Reference extract at the end that they continue to work closely with the military in Europe and are continuing to develop and exploit spiking neural networks for cybersecurity connected with critical infrastructure. It is not a big stretch to link Brainchip and its AKIDA technology solutions particularly given their presence in France as well.

My opinion only DYOR

FF

AKIDA BALLISTA

Dr. Konstantios Demertzis

· BSc in MilitaryScience1996

Academy of Non-Commissioned Officers

Academy of Non-Commissioned Officers

Experience

· PostdoctoralResearch Fellow in CyberSecurity and Critical Infrastructure Protection2017 - Present

Democritus University of Thrace,School of Civil Engineering, Faculty of Mathematics, Programming and generalcourses, Xanthi (Greece)

Postdoctoral Research Fellow,Democritus University of Thrace, School of Engineering, Department of CivilEngineering

· Chief InformationSecurity Officer at the Research & Informatics Directorate2018 - Present

Greek Army, Didymoteicho (Greece)

Chief Information Security Officer atthe Research & Informatics Directorate, Greek Army

A Computational Intelligence System IdentifyingCyber-Attacks on Smart Energy Grids

Konstantinos Demertzis1 , Lazaros Iliadis2 1,2 School of Engineering, Department of Civil Engineering, Faculty of Mathematics Programming and General courses, Democritus University of Thrace, Kimmeria, Xanthi, Greece

7. Discussion – Conclusions

An innovative biologically inspired hybrid computational intelligence approach suitable for big data was presented in this research paper. It is a computational intelligence system for identification cyber-attacks on Smart Energy Grids. Specifically, the hybrid and innovative AEDE-ELM algorithm was suggested which uses the innovative and highly effective algorithm AEDE in order to optimize the operating parameters of an ELM. The classification performance and the accuracy of the proposed model were experimentally explored based on several scenarios and reported very promising results. Moreover, SICASEG is an effective cross-layer system of network supervision, with capabilities of automated control. This is done to enhance the energetic security and the mechanisms of reaction of the general system, without special requirements. In this way, it adds a higher degree of integrity to the rest of the security infrastructure of Smart Energy Grids. The most significant innovation of this methodology is that it offers high learning speed, ease of implementation, minimal 16 human intervention and minimum computational power and resources to properly classify SCADA attacks with high accuracy and generalization. Future research could involve its model under a hybrid scheme, which will combine semi supervised methods and online learning for the trace and exploitation of hidden knowledge between the inhomogeneous data that might emerge. Also, SICASEG could be improved towards a better online learning with self-modified the number of hidden nodes. Moreover, additional computational intelligence methods could be explored, tested and compared on the same security task in an ensemble approach. Finally, the ultimate challenge would be the scalability of SICASEG with other bio-inspired optimization algorithms in parallel and distributed computing in a real-time system.

References:

[16] Demertzis K., Iliadis L. (2014). A Hybrid NetworkAnomaly and Intrusion Detection Approach Based on Evolving Spiking NeuralNetwork Classification. In: E-Democracy, Security, Privacy and Trust in a Digital World. Communications in Computer and Information Science, 441, 11-23. doi:10.1007/978-3-319-11710-2_2 [17] Demertzis K., Iliadis L. (2014). Evolving Computational Intelligence System for Malware Detection, In: Advanced Information Systems Engineering Workshops, Lecture Notes in Business Information Processing, 178, 322-334. doi: 10.1007/978-3-319-07869-4_30 [18] Demertzis K., Iliadis L. (2014, April). Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security. Springer Proceedings 2ndConference on CryptAAF: Cryptography Network Security and Applications in theArmed Forces, Springer, Athens, 161- 193. doi: 10.1007/978-3-319-18275-9_7