Events

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

Speaker: Erol Gelenbe

Location: 370 Jay Street, Room 825
Videoconference link: https://nyu.zoom.us/j/97992879944

Date: Thursday, October 12, 2023

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).