Abdelhamied Ashraf Ateya

Abstract The IoT has posed novel cyber-physical vulnerabilities due to the fast proliferation of Internet of Things (IoT) systems. Old network-based intrusion detection solutions can poorly identify malicious activities that are caused by on-device sensors. This paper introduces a multimodal sensing architecture based on deep learning to identify cyber-attacks on the traces of heterogeneous sensors, such as acceleration, gyroscopes, microphones, and temperature devices.

1. Introduction Infectious diseases continue impacting a vast global population, despite ongoing advancements in treatment and prevention. Efficient control necessitates the precise management of factors such as transmissibility, population size, contact rates, and infection duration. One of the most important aspects of understanding and managing illness processes is mathematical modeling, particularly when vaccination is inaccessible or during the early stages of a disease.