Detecting Cyber Attacks in Smart Grids Using Semi-Supervised Anomaly Detection and Deep Representation Learning
| العنوان: | Detecting Cyber Attacks in Smart Grids Using Semi-Supervised Anomaly Detection and Deep Representation Learning |
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| المؤلفون: | Jun Zheng, Ruobin Qi, Craig Rasband, Raul Longoria |
| المصدر: | Information, Vol 12, Iss 328, p 328 (2021) Information Volume 12 Issue 8 |
| بيانات النشر: | MDPI AG, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Computer science, Blackout, Information technology, Machine learning, computer.software_genre, cyber-physical systems, deep representation learning, Electric power system, semi-supervised anomaly detection, medicine, smart grids, business.industry, Supervised learning, Cyber-physical system, deep autoencoder (DAE), T58.5-58.64, Smart grid, Cyber-attack, Anomaly detection, Artificial intelligence, medicine.symptom, business, computer, Feature learning, cyber attacks, Information Systems |
| الوصف: | Smart grids integrate advanced information and communication technologies (ICTs) into traditional power grids for more efficient and resilient power delivery and management, but also introduce new security vulnerabilities that can be exploited by adversaries to launch cyber attacks, causing severe consequences such as massive blackout and infrastructure damages. Existing machine learning-based methods for detecting cyber attacks in smart grids are mostly based on supervised learning, which need the instances of both normal and attack events for training. In addition, supervised learning requires that the training dataset includes representative instances of various types of attack events to train a good model, which is sometimes hard if not impossible. This paper presents a new method for detecting cyber attacks in smart grids using PMU data, which is based on semi-supervised anomaly detection and deep representation learning. Semi-supervised anomaly detection only employs the instances of normal events to train detection models, making it suitable for finding unknown attack events. A number of popular semi-supervised anomaly detection algorithms were investigated in our study using publicly available power system cyber attack datasets to identify the best-performing ones. The performance comparison with popular supervised algorithms demonstrates that semi-supervised algorithms are more capable of finding attack events than supervised algorithms. Our results also show that the performance of semi-supervised anomaly detection algorithms can be further improved by augmenting with deep representation learning. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2078-2489 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd98b80e8adf7ed6674e1158679d964e https://www.mdpi.com/2078-2489/12/8/328 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....fd98b80e8adf7ed6674e1158679d964e |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20782489 |
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