Framework for Securing Crowdsourcing Platform for Internet of things using Machine Learning

Abstract

Purpose

The purpose of this research is to address security challenges in IoT-based crowdsourcing platforms by developing a dynamic and adaptive security framework. Traditional security policies struggle to cope with the ever-changing and heterogeneous nature of IoT environments. This study aims to explore how Reinforcement Learning (RL) can enhance security by continuously adapting to emerging threats while maintaining system efficiency.

Objective

The primary objective is to design and implement an RL-based security framework that optimizes security policies in real-time. This framework seeks to improve security efficacy, reduce resource consumption, and enhance the system’s ability to quickly respond to new threats. By achieving these objectives, the research contributes to the development of more resilient IoT crowdsourcing platforms.

Methodology

The proposed framework utilizes RL algorithms to dynamically adjust security policies based on observed threats and system conditions. The model is trained on real-time data from IoT crowdsourcing environments and continuously learns optimal security responses. The study involves implementing and testing various RL models to compare their effectiveness in securing the platform while maintaining operational efficiency.

Try-outs

The framework is evaluated through a series of experiments measuring Security Efficacy, Resource Efficiency, and Adaptation Speed. The experiments simulate various attack scenarios and system conditions to assess how well the RL-based framework adapts to threats. The results demonstrate that the proposed approach significantly enhances security performance, optimizes resource usage, and enables faster adaptation compared to static security policies.