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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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ARIES: A Novel Multivariate Intrusion Detection System for Smart Grid.

Panagiotis Radoglou Grammatikis1, Panagiotis Sarigiannidis1, Georgios Efstathopoulos2

  • 1Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece.

Sensors (Basel, Switzerland)
|September 19, 2020
PubMed
Summary

We developed ARIES, a novel Intrusion Detection System (IDS) for Smart Grids (SG). ARIES uses machine learning to detect cyberattacks on network flows, Modbus/TCP packets, and operational data, enhancing SG cybersecurity.

Keywords:
Intrusion Detection SystemMachine LearningModbusSCADASmart Gridcybersecurity

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Cybersecurity

Background:

  • Smart Grids (SG) face significant cybersecurity risks.
  • Existing security measures may not adequately protect SG communications.
  • Novel intrusion detection systems are crucial for SG resilience.

Purpose of the Study:

  • To introduce ARIES (smArt gRid Intrusion dEtection System), an advanced anomaly-based Intrusion Detection System (IDS).
  • To enhance the security of Smart Grid communications against sophisticated cyber threats.
  • To leverage Machine Learning (ML) for multi-layered threat detection within SG environments.

Main Methods:

  • ARIES employs a three-layered detection approach: network flow analysis, Modbus/TCP packet inspection, and operational data monitoring.
  • Machine Learning models, including a Generative Adversarial Network (GAN) with novel input and error minimization, are trained on power plant data.
  • The system identifies Denial of Service (DoS) attacks, brute force attacks, port scanning, bots, Modbus anomalies, and operational data deviations.

Main Results:

  • The network flow layer performs supervised multiclass classification for known attack types.
  • The packet and operational data layers detect anomalies in Modbus traffic and time-series electricity measurements.
  • The ARIES GAN demonstrated superior performance over conventional ML methods, achieving higher Accuracy and F1 scores.

Conclusions:

  • The proposed ARIES system effectively detects a range of cyberattacks and anomalies in Smart Grid communications.
  • The ARIES GAN, particularly its third layer focusing on operational data, offers enhanced detection capabilities.
  • This research contributes a robust solution for improving the cybersecurity posture of Smart Grids.