Step-gan : A one-class anomaly detection model with applications to power system security

• Smart grid systems (SGSs), and in particular power systems, play a vital role in today's urban life. The security of these grids is now threatened by adversaries that use false data injection (FDI) to produce a breach of availability, integrity, or confidential principles of the system. We propose a novel structure for the multigenerator generative adversarial network (GAN) to address the challenges of detecting adversarial attacks. We modify the GAN objective function and the training procedure for the malicious anomaly detection task. The model only requires normal operation data to be trained, making it cheaper to deploy and robust against unseen attacks. Moreover, the model operates on the raw input data, eliminating the need for feature extraction. We show that the model reduces the well-known mode collapse problem of GAN-based systems, it has low computational complexity and considerably outperforms the baseline system (OCAN) with about 55% in terms of accuracy on a freely available cyber attack dataset.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Anomaly Detection

Cyber Attacks

Mode Collapse

One-Class Classification

Power Systems

Security

Electric power system security

Electric power transmission networks

Security of data

Signal processing

Smart power grids

Adversarial networks

Anomaly detection models

False data injection

Low computational complexity

Objective functions

Power system security

Smart grid systems

Training procedures

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