Generative-Adversarial Class-Imbalance Learning for Classifying Cyber-Attacks and Faults - A Cyber-Physical Power System
| Autor: | Maryam Farajzadeh-Zanjani, Roozbeh Razavi-Far, Ehsan Hallaji, Mehrdad Saif |
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| Rok vydání: | 2021 |
| Předmět: |
Standards
Cyber-Attacks Cyber-Physical Systems Faults Generative adversarial networks Computer science Cyber-physical system 02 engineering and technology Computer security computer.software_genre Generators Predictive models Electric power system Adversarial system Class imbalance 020204 information systems Task analysis 0202 electrical engineering electronic engineering information engineering Training Intrusion detection Electrical and Electronic Engineering Generative-Adversarial Networks computer Generative grammar Class-Imbalance Learning |
| Zdroj: | Electrical and Computer Engineering Publications |
| Popis: | There has been an increasing interest in the use of data-driven techniques for classifying cyber-attacks and physical faults in cyber-physical systems. In real-world applications, the number of cyber-attack and faulty samples is usually far less than normal samples. This causes the skewed class distribution in data collected from cyber-physical systems. Training an accurate predictive model under skewed class conditions is not an easy task. In this work, we introduce a new generative adversarial framework for learning from skewed class distributions. This novel Adversarial Class-Imbalance Learning (ACIL) scheme has a novel loss function that is used during the adversarial training session. ACIL tries to iteratively adjust weights of an auxiliary multilayer perceptron to learn the minority class (i.e., cyber-attacks and physical faults) distributions along with the majority class (i.e., normal) distribution. Moreover, we devise an inclusive data-driven scheme for classifying cyber-attacks and faults, which includes four experiments of a baseline, nine state-of-the-art class-imbalance learning methods, two different generative-adversarial network-based approaches, and ACIL. These techniques are verified and compared through several experimental cyber-physical power scenarios. The obtained results show the effectiveness of ACIL for classifying samples of cyber-attacks and faults with skewed class distributions. |
| Databáze: | OpenAIRE |
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