Enhancing Network Security using MachineLearning for Automated Anomaly-based Intrusion Detection Systems for IoT Environment

Abstract

The 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.