Building AI-Driven Anomaly Detection Model to Secure Industrial Automation

Introduction

In modern industrial automation, security is a primary requirement to keep the regular operation of industrial connected devices without disruption. However, the rise of cyber risks also significantly impacts the industry’s sustainable operation. The evolving cyberattacks can affect the overall industrial systems that control industrial processes and systems. Modern attacks are more targeted and designed to evade detection by traditional defensive approaches. A proactive approach is necessary, rather than a defensive strategy, to tackle these evolving cyber threats. This article presents a use case for building an anomaly detection framework using artificial intelligence (AI). More specifically, a hybrid learning model consisting of a deep learning LSTM model for feature extraction and a machine learning (ML) classifier to detect and predict anomalous behavior in industrial automation. 

The evolution of next-generation technologies, also known as Industry 4.0, has evolved to meet the challenges and requirements of optimal operations and efficient sustainability in industrial automation networks. In this modern era, the development of advanced mobile networks (5G), big data analytics, the Internet of Things (IoT), and Artificial Intelligence (AI) provides excellent opportunities for better and more optimal industrial operations. The integration of Mobile Network, for example, enables the seamless operation of millions of IIoT devices connected simultaneously with minimal bandwidth and low latency. However, apart from excellent opportunities, these technological paradigms also open a new door to cyber-criminals that can affect the sustainability and operations of industrial networks.