A Performance Comparison of Data Mining Algorithms Based Intrusion Detection System for Smart Grid
| Autor: | Zakaria El Mrabet, Naima Kaabouch, Hassan El Ghazi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Computer Science - Cryptography and Security Computer science 0211 other engineering and technologies Machine Learning (stat.ML) 02 engineering and technology Intrusion detection system computer.software_genre Statistical power Machine Learning (cs.LG) Naive Bayes classifier Statistics - Machine Learning FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing 021103 operations research business.industry Information technology Random forest Smart grid 020201 artificial intelligence & image processing Electric power Data mining False alarm business Cryptography and Security (cs.CR) computer |
| Zdroj: | EIT |
| DOI: | 10.1109/eit.2019.8834255 |
| Popis: | Smart grid is an emerging and promising technology. It uses the power of information technologies to deliver intelligently the electrical power to customers, and it allows the integration of the green technology to meet the environmental requirements. Unfortunately, information technologies have its inherent vulnerabilities and weaknesses that expose the smart grid to a wide variety of security risks. The Intrusion detection system (IDS) plays an important role in securing smart grid networks and detecting malicious activity, yet it suffers from several limitations. Many research papers have been published to address these issues using several algorithms and techniques. Therefore, a detailed comparison between these algorithms is needed. This paper presents an overview of four data mining algorithms used by IDS in Smart Grid. An evaluation of performance of these algorithms is conducted based on several metrics including the probability of detection, probability of false alarm, probability of miss detection, efficiency, and processing time. Results show that Random Forest outperforms the other three algorithms in detecting attacks with higher probability of detection, lower probability of false alarm, lower probability of miss detection, and higher accuracy. Comment: 6 pages, 6 Figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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