|Speaker :||Erol Gelenbe|
|Institute of Theoretical & Applied Informatics, Polish Academy of Sciences, & Laboratoire I3S Univ. Cote d’Azur|
|Time:||3:00 pm - 4:00 pm|
The relative simplicity and lightweight nature of many IoT devices, and their widespread connectivity via the Internet and other wired and wireless networks, raise issues regarding both their performance and vulnerability. Indeed, their own connectivity patterns based on the need to frequently forward and receive, data has given rise to the « Massive Access Problem (MAP) of the IoT » which is a form of congestion caused by the IoT’s synchronized and repetitive data transmission patterns. On the other hand, the opportunity that IoT devices present to malicious third parties for generating highly contagious distributed denial of service (DDoS) and Botnet attacks, is also a subject of concern which is widely studied. Thus this presentation will discuss our recent results and research directions that address both of these issues. Regarding the MAP, we will outline the Quasi-Deterministic Transmission Policy (QDTP), and its main theoretical result, and present trace driven measurements, which show that QDTP can effectively mitigate MAP. We will also show how a Machine Learning approach using novel Auto-Associative Dense Random Neural Networks can detect DDos attacks with a high degree of accuracy, and discuss the potential of « low cost » online learning to protect IoT gateways and devices against Cyberattacks. The speaker gratefully acknowledges research funding from the EC as part of the H2020 GHOST, SerIoT and IoTAC projects.
Info on the speaker: https://en.wikipedia.org/wiki/Erol_Gelenbe