Enhancing IoT Security with AI-Driven Hybrid Machine Learning and Neural Network-Based Intrusion Detection System

Abstract

The increasing occurrence of cyberattacks specifically aimed at critical infrastructure has led to the adoption of network intrusion detection techniques for the Internet of Things (IoT). Securing IoT networks is difficult because of the growing number of connected devices and the advanced methods used by attackers. This study investigates the application of machine learning and neural networks in the prevention of prevalent online fraud and assesses their efficacy. The text discusses important ideas related to email filtering, machine learning, artificial neural networks, and network intrusion techniques. The study discusses the difficulties related to e-fraud detection and suggests methods to improve detection systems. Furthermore, it offers a thorough examination of IoT intrusion detection, emphasizing the risks, weaknesses, assaults, and methods of detection. Securing the billions of autonomous nodes in the Internet of Things (IoT), each with distinct characteristics, poses a significant challenge. Conventional techniques like as encryption, access control, and authentication are inadequate when used individually. Thus, this work utilizes deep learning techniques to detect widespread IoT vulnerabilities, such as Distributed Denial of Service (DDoS) assaults. The models are evaluated using different datasets, including NSL-KDD, DS2OS, and IoT Botnet. The evaluation is based on measures such as accuracy, precision, recall, and F1-score. The deep machine learning intrusion detection system has a high accuracy rate of 96.38%, which shows its efficiency in recognizing risks related to the Internet of Things (IoT) Where the data was trained by 80% and the data was tested by 20.