Enhancing Intrusion Detection Capabilities in IoT Technologies through Machine Learning
Imran Hasan, Abdullah All Ahhad, Sk Md Rakibul Islam, Marufa Akhter Mim, Md Habibur Rahman · 2024
Smart home systems, industrial automation, healthcare systems, and others have been ground sectors developed massively because of the exponential growth that the Internet of Things (IoT) has had. The significant issues that it has presented are security challenges. IoT devices are weakly defended in terms of security and widely susceptible to cyber threats because of interconnectivity. This paper presents novel methods to mitigate such risks, with their implementation focused on intrusion detection systems that can be enhanced with Random Forest classifiers to detect and terminate malicious activities. The approach presented attains excellent performance, with an accuracy of 98.2% in the case of the NSL-KDD dataset and 99.9% for the BoT-IoT dataset. The paper discloses improved accuracy, precision, and recall relative to the existing approaches in the field and that RF classifiers effectively enhance the IDS capability. The importance of flexibility among ML models is therefore highlighted in the real-time detection and reaction to prevent significant damages and data breaches in IoT environments. The paper also discusses the deployment alternatives because of the limitation of resources in IoT devices, with which an architectural implementation of the IDS framework will raise challenges toward real-world IoT applications. Effective methods for overhead and accurate detection efficacy were also recommended. These results are regarded as an addition to the debate on IoT security and have been helpful to researchers, practitioners, and policymakers. This state-of-the-art machine learning technique is protection proposed for the IoT ecosystem not only from present threats but also from futuristic challenges to ensure the most secure IoT systems. Research contributes to innovation and continuous improvement by securing a growing IoT and future advanced systems through cyber collaboration