# Devil in the Detail: Attack Scenarios in Industrial Applications

## TL;DR

This paper categorizes attack vectors in industrial networks, simulates attacks on a real-world process, and compares machine learning methods for detecting these attacks, highlighting the effectiveness of Matrix Profiles over LSTM networks.

## Contribution

It introduces a comprehensive categorization of attack scenarios in industrial networks and evaluates machine learning techniques for anomaly detection in this context.

## Key findings

- Matrix Profiles outperform LSTM in attack detection
- Simulated attacks demonstrate vulnerabilities in industrial networks
- Effective anomaly detection methods can enhance industrial cybersecurity

## Abstract

In the past years, industrial networks have become increasingly interconnected and opened to private or public networks. This leads to an increase in efficiency and manageability, but also increases the attack surface. Industrial networks often consist of legacy systems that have not been designed with security in mind. In the last decade, an increase in attacks on cyber-physical systems was observed, with drastic consequences on the physical work. In this work, attack vectors on industrial networks are categorised. A real-world process is simulated, attacks are then introduced. Finally, two machine learning-based methods for time series anomaly detection are employed to detect the attacks. Matrix Profiles are employed more successfully than a predictor Long Short-Term Memory network, a class of neural networks.