Modeling and forecasting overdispersed IoT data using an efficient and accurate computation of the Dirichlet Multinomial distribution
Year: 2025
Type: conference-paper
Abstract: The Internet of Things (IoT) has rapidly become a ubiquitous aspect of modern society, intertwining with our daily lives in many ways; while these advancements provide several beneficial effects, they also bring forth significant security concerns that must be addressed to safeguard sensitive data, ensure privacy, and prevent potential cyber threats. While security by design will undoubtedly help in hardening systems, security is and will remain an arm-race between attackers and defenders, hence, a most important feat in the field of security for networked systems is the capability to model traffic and provide predictions capable of identifying suspicious behavior. In this paper we model the overdispersed traffic of data collected from a device belonging to an IoT system using the Dirichlet Multinomial distribution and use that model to forecast the device traffic for the next 24 hours. Also, we compare the efficiency and accuracy of some state of the art techniques in generating traffic predictions.
Institutions: University of Jordan, University of Padua
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410342427