计算机科学与系统生物学杂志

Efficient Anomaly Detection in Industrial LoT Networks Using Data Mining and Machine Learning

Abstract

Julius Meroni*

The Industrial Internet of Things has revolutionized the way industries operate by providing real-time data from various sensors and devices. However, the vast amount of data generated in IIoT networks poses a significant challenge in identifying anomalies and potential security threats. In this research article, we explore the use of data mining and machine learning techniques for efficient anomaly detection in IIoT networks. We present a comprehensive analysis of various methodologies and tools that can be employed to enhance the security and reliability of industrial systems. Our findings suggest that a combination of feature engineering, supervised learning, and unsupervised learning techniques can lead to highly effective and efficient anomaly detection systems.