Random Neural Networks (RNN) for Accurate CyberAttack Detection and Mitigation at the Edge

Lecture / Panel
For NYU Community

Erol Gelenbe smiling for the camera


Prof. Erol Gelenbe
Academia Europaea


"Random Neural Networks (RNN) for Accurate CyberAttack Detection and Mitigation at the Edge"


Even simple cyberattacks can impair the operation and performance of network systems substantially for many hours and sometimes days, and also increase the system's energy consumption. Their impact on data security, and the effects of the malware that they convey and install, are also well known. Thus there is a widespread need for accurate cyberattack detection, and rapid reaction and mitigation when attacks occur. On the other hand, the detection must avoid false alarms, to avoid impairing the smooth operation of a system which is not under attack. Thus considerable research has been conducted in this important field. Our presentation will briefly introduce the subject, and then focus on some recent results from the last 4-5 years, that are based on the Random Neural Network (RNN). The mathematical model will be described, and its extensions and deep learning algorithms will be discussed in the context of cyberattack detection and mitigation. The presentation will then focus on practical applications illustrated with different cyberattack datasets, showing the high accuracy and low false alarm rates that can be achieved. Measurements of active control schemes for attack mitigation will also be shown. Finally we will also show how the RNN can be used with Reinforcement Learning and SDN (Software Defined Networks), to dynamically control an Edge System that optimizes Security, QoS and Energy Consumption.

Note. The talk will be based on our publications in the following journals and conferences: Proceedings of the IEEE (2020), Sensors (2021, 2023), ACM SIGCOMM Flexnets (2021), ICC (2016, 2022), IEEE Access (2022, 2023), Performance Evaluation (2022).

About Speaker

Institute of Theoretical & Applied Informatics, Polish Academy of Sciences, & University Côte d'Azur I3S CNRS, 06100 Nice, France

Erol Gelenbe FIEEE FACM FIFIP FRSS received his MS and PhD degrees at Brooklyn Poly, and won the Tandon School Distinguished Alumnus Award in 2010. He has held named personal chairs at NJIT (USA), Duke University (USA), University of Central Florida (USA), Imperial College (UK), and full professorships at the University of Liege, University Paris Saclay, and University Paris Descartes. He served as Department Head at University Paris Descartes (1986-92), Duke University (1993-1998), and Director of the School of EECS at UCF (1993-1998). His research focuses on QoS, Security and Energy, and was funded by Industry, DoD and NSF in the USA, EPSRC and MoD in the UK, and numerous EU FP5, FP6, FP7, and Horizon 2020 projects since 2003. Currently Professor at the Institute of Theoretical & Applied Informatics, Polish Academy of Sciences, since 2017, he also cooperates with the CNRS I3S Laboratory of University Côte d'Azur (Nice, France), and Yasar University (Izmir, Turkey), and his work is now supported by grants from H2020 Horizon and UKRI. He is ranked among the top 25 PhD advisors in mathematical sciences by the American Mathematical Society Math. Genealogy Project and has graduated 24 women PhDs. Winner of the Grand Prix France Telecom 1996 (French Academy of Sciences), the ACM SIGMETRICS 2008 Life-Time Award, and the Mustafa Prize 2017, he was awarded the high honors of Commander of the Order of the Crown, Belgium (2022), Commander of the Order of Merit, France (2019), Knight of the Legion of Honour, France (2014), Commander of the Order of Merit, Italy (2005), Grand Officer of the Order of the the Star, Italy (2007). Fellow of the French National Academy of Engineering, and of the Science Academies of Belgium, Poland and Turkey, he is also Honorary Fellow of the Hungarian and Islamic Academy of Sciences. He currently chairs the Informatics Section of Academia Europaea.