Deep Learning Approaches for Ensuring Secure Task Scheduling in IoT Systems

Abstract

This research focuses on the challenge of secure task scheduling in IoT systems and proposes a novel framework that leverages deep learning techniques to address the security concerns arising from the dynamic nature of IoT devices and their interactions. The framework follows a systematic workflow that involves data collection, feature extraction, deep learning model training, and real-time validation. Data related to task scheduling, device information, network conditions, and security parameters are collected, and important features are extracted to capture essential characteristics. A deep learning model is then trained using a labeled dataset to accurately predict the security implications of task scheduling decisions. The trained model is integrated into the task scheduler of the IoT system to continuously analyze new task scheduling decisions in real-time and provide predictions on their security status. If insecure decisions are detected, appropriate actions can be taken to mitigate potential security risks. The framework ensures continuous learning and adaptation by periodically updating the deep learning model with new data. Experimental evaluations demonstrate the effectiveness of the approach in enhancing IoT security, with significant improvements in detecting and preventing security vulnerabilities. By incorporating deep learning into task scheduling, this research enables advanced security analysis and decision support, proactively mitigating security risks and creating a secure and reliable IoT environment.