Mahamud, N.Uddin, M. J.Sumaiya, U.2025-10-082025Mahamud, N., Uddin, M. J., & Sumaiya, U. (2025). Enhancing network security using machinelearning for automated anomaly-based intrusion detection systems for IoT environment. In Proceedings of the International Research Conference on Smart Computing and Systems Engineering (SCSE 2025). Department of Industrial Management, Faculty of Science, University of Kelaniya.http://repository.kln.ac.lk/handle/123456789/30056The rapid expansion of the Internet of Things (IoT) has revolutionized modern life, offering unparalleled automation and seamless interconnectivity between devices, often operating without user intervention. However, this convenience comes with a significant trade-off: increased susceptibility of IoT devices to cyberattacks, which can result in severe consequences if not promptly addressed. To tackle this pressing challenge, our study proposes innovative strategies powered by machine learning algorithms, achieving an exceptional 99.97% detection accuracy and a 0.0% false positive rate. Leveraging the Bot-IoT dataset for evaluation, our approach demonstrates marked improvements over existing detection methodologies. Furthermore, its adaptability to diverse IoT applications underscores its potential as a transformative advancement in IoT security.Cyber-securityInternet of Things (IoT)Intrusion Detection Systems (IDS)Machine learning (ML)Anomaly detectionArticle