Year: 2022
Type: conference-paper
Abstract: Modbus/TCP is a communication protocol extensively used in machine-to-machine communications. Therefore, anomaly detection on this protocol is of paramount importance in the industry as it helps protect cyber assets and monitor IoT devices created by manufacturing plants across the globe. This detection is achieved by looking at standard patterns and then comparing them with patterns of data found unknown to the system itself. Today, catapulted by rapid developments in machine learning, there has been a marked reduction in the time it takes to identify anomalies in Modbus/TCP networks. Anomaly detection methodologies, leveraging neural networks trained through numerous machine-learning algorithms, further provide a system that could be effectively applied to many IoT device marketplaces, such as consumer-level applications. This paper proposes a framework for evaluating feature sets and learning-based models to detect Modbus/TCP anomalies efficiently and accurately.
Institution: B.M.S. College of Engineering
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4379932949