"Cognitive Packet Networks Adaptively Defend Against Cyber-Attacks"

No próximo dia 1 de outubro de 2020, pelas 14h30, no Anfiteatro Ferreira da Silva (Anfiteatro 1) no DCC, o Prof. Erol Gelenbe irá dar uma palestra intitulada "Cognitive Packet Networks Adaptively Defend Against Cyber-Attacks".

A palestra é organizada pelo DCC-FCUP e é aberta a todos os interessados.

A inscrição é obrigatória.

As normas sanitárias da FCUP contra COVID-19 devem ser seguidas: distanciamento social, uso de máscara e higienização das mãos.


Short Bio

Erol Gelenbe is a Full Professor in the Institute of Theoretical and Applied Informatics of the Polish Academy of Sciences, affiliated to the Computer Systems Modelling and Performance Evaluation Group. He is also affiliated with the I3S Laboratory of the University of Cote d'Azur (Nice) and Fellow of the IEEE, ACM, the Royal Statistical Society, IFIP and IET (London).


Gelenbe has contributed pioneering research concerning the performance of multiprogramming computer systems, virtual memory management, data base reliability optimisation, distributed systems and network protocols. He formed, led, and trained the team that designed the commercial QNAP Computer and Network Performance Modeling Tool. He introduced the Flexsim Object Oriented approach for the simulation in manufacturing systems. He carried out some of the first work on adaptive control of computer systems, and published seminal papers on the performance optimisation of computer network protocols and on the use of diffusion approximations for network performance. He developed new product form queueing networks with negative customers and triggers known as G-networks. He also introduced a new spiked stochastic neural network model known as the random neural network, developed its mathematical solution and learning algorithms, and applied it to both engineering and biological problems. His inventions include the design of the first random access fibre-optics local area network, a patented admission control technique for ATM networks, a neural network based anomaly detector for brain magnetic resonance scans, and the cognitive packet network routing protocol to offer quality of service to users.


Since 2019 he pursues an active European career as Coordinator (Principal Investigator) of the EU H2020 Research and Innovation Project SerIoT (2017-2021) on the security of the Internet of Things, Co-Principal Investigator of the EU H2020 Research and Innovation Programmes SDK4ED (2018-2020) and of the H2020 IoTAC Project (2020-2023). His research appears in leading international journals and conferences. He is the Co-Editor in Chief of Springer-Nature Computer Science. He is active in the European National Academies as Section Member for Informatics of Academia Europaea (since 2017), SAPEA Advisor on Cybersecurity for the EU High Level Group (2017), Member of the Fake-News Study Group of the All-European Academies (ALLEA, 2020-2021), and leading the Science Communication (Diffusion des Sciences) of the Royal Belgian Academy of Sciences and the Association of European Academies of Engineering (EuroCase).



"Cognitive Packet Networks Adaptively Defend Against Cyber-Attacks"



The need to adaptively manage computer sys-tems and networks so as to offer good Quality of Service (QoS) and Quality of Experience (QoE) with secure operationat relatively low levels of energy consumption is challengedby their sheer complexity and the wide variability of the workloads. A possible way forward is through self-awareness,where by self-measurement and self-observation, together with on-line control mechanisms, operate adaptively to attain therequired performance and QoE. We survey the premises for these ideas arising from cognitive science and active networks and review recent work on self-aware computer systems and networks, including those that propose the use of software-defined networks as a means to implement these concepts.Then we provide some examples from the literature on self-aware systems to illustrate the performance gains that they can provide. Finally, we detail an example system and its working algorithms to allow the reader to understand how such a system may be implemented. Measurements showing how it can react rapidly to changing network conditions regarding QoS and security are presented. Some conclusions and sug-gestions for further work are listed.

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