Enhancing Cybersecurity in IoT Networks: A Machine Learning-Based Intrusion Detection Approach

Abstract

This research addresses the growing concern of cybersecurity in the Internet of Things (IoT) networks, where the proliferation of connected devices presents significant security challenges. The study aims to enhance IoT network security by proposing a machine learning-based intrusion detection system (IDS) to identify and mitigate potential threats. A comprehensive analysis of IoT vulnerabilities was conducted, followed by the development of an IDS model utilizing various machine learning algorithms, such as decision trees, random forests, and support vector machines. The proposed approach was evaluated using publicly available IoT datasets, demonstrating its effectiveness in detecting a wide range of cyber threats with high accuracy and low false-positive rates. The findings suggest that machine learning techniques can significantly improve the detection of intrusions in IoT environments, providing a robust solution for securing IoT networks. The research emphasizes the potential of integrating machine learning with IoT security to create adaptive, intelligent defense mechanisms against evolving cyber threats, ultimately contributing to the advancement of IoT network protection strategies.