• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Artikel
  4. Lightweight Long Short-Term Memory Variational Auto-Encoder for Multivariate Time Series Anomaly Detection in Industrial Control Systems
 
  • Details
  • Full
Options
2022
Journal Article
Title

Lightweight Long Short-Term Memory Variational Auto-Encoder for Multivariate Time Series Anomaly Detection in Industrial Control Systems

Abstract
Heterogeneous cyberattacks against industrial control systems (ICSs) have had a strong impact on the physical world in recent decades. Connecting devices to the internet enables new attack surfaces for attackers. The intrusion of ICSs, such as the manipulation of industrial sensory or actuator data, can be the cause for anomalous ICS behaviors. This poses a threat to the infrastructure that is critical for the operation of a modern city. Nowadays, the best techniques for detecting anomalies in ICSs are based on machine learning and, more recently, deep learning. Cybersecurity in ICSs is still an emerging field, and industrial datasets that can be used to develop anomaly detection techniques are rare. In this paper, we propose an unsupervised deep learning methodology for anomaly detection in ICSs, specifically, a lightweight long short-term memory variational auto-encoder (LW-LSTM-VAE) architecture. We successfully demonstrate our solution under two ICS applications, namely, water purification and water distribution plants. Our proposed method proves to be efficient in detecting anomalies in these applications and improves upon reconstruction-based anomaly detection methods presented in previous work. For example, we successfully detected 82.16% of the anomalies in the scenario of the widely used Secure Water Treatment (SWaT) benchmark. The deep learning architecture we propose has the added advantage of being extremely lightweight.
Author(s)
Fährmann, Daniel  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Damer, Naser  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Kirchbuchner, Florian  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Kuijper, Arjan  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Journal
Sensors. Online journal  
Project(s)
Secure Urban Infrastructures
Funder
Bundesministerium für Bildung und Forschung -BMBF-  
Open Access
DOI
10.3390/s22082886
File(s)
2022 Fährmann Lightweight .pdf (533.09 KB)
Rights
CC BY
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Lead Topic: Smart City

  • Research Line: Machine Learning (ML)

  • Manufacturing industry applications

  • Machine learning

  • Security technologies

  • Cyber security

  • Deep learning

  • ATHENE

  • CRISP

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024