Improvement of IoT Security with a Machine Learning-Based Intrusion Detection System Approach

Abstract

The development of the Internet of Things (IoT) has brought convenience to various aspects of life, but it also presents significant challenges regarding cybersecurity. One solution to address this issue is the development of an Intrusion Detection System (IDS) based on machine learning. This study aims to design an efficient and adaptive IDS for IoT environments using machine learning algorithms such as Random Forest and Support Vector Machine (SVM). The methodology includes system design, data collection, algorithm selection, model training, and system performance evaluation. The results show that Random Forest and SVM algorithms are effective in detecting attacks such as Distributed Denial of Service (DDoS) and malware, with a relatively high accuracy rate. However, the main challenges faced are the need for representative datasets and computational efficiency issues on resource-constrained IoT devices. This study concludes that machine learning-based intrusion detection systems can improve IoT security by accurately detecting cyber-attacks. Further development is expected to address efficiency constraints and enhance the system's reliability in facing increasingly complex threats.