A Machine Learning Framework for Intrusion Detection in IOT Environments

Project Code :TCMAPY1058

Objective

The primary objective of this project is to develop a robust Machine Learning Framework for Intrusion Detection in IoT Environments. This framework will be designed to effectively identify and respond to intrusion attempts in IoT systems, ensuring the integrity and security of connected devices and data. The project aims to enhance the existing state of IoT security by leveraging machine learning techniques for more adaptive and accurate intrusion detection. Through rigorous experimentation and evaluation, the objective is to demonstrate the framework's efficacy in mitigating threats and providing a scalable and sustainable solution for safeguarding IoT ecosystems against evolving security challenges.

Abstract

In an increasingly interconnected world, securing Internet of Things (IoT) environments against intrusions is paramount. This paper presents a novel Machine Learning Framework for Intrusion Detection in IoT Environments. Leveraging curated datasets, we employ data preprocessing and feature engineering techniques to enhance data quality and relevance. Our framework employs a suite of machine learning algorithms for accurate intrusion detection. Experimental results demonstrate superior performance compared to baseline methods, achieving high accuracy, precision, and recall. This research advances IoT security, offering a robust solution to safeguard IoT ecosystems.

Keywords: IoT security, intrusion detection, machine learning, data preprocessing, feature engineering.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.