| Title | DoS attack detection model of smart grid based on machine learning method |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Zhe, Wang, Wei, Cheng, Chunlin, Li |
| Conference Name | 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) |
| Date Published | jul |
| Keywords | Classification algorithms, composability, DoS attacks, feature extraction, Intrusion detection, machine learning, privacy, pubcrawl, Resiliency, Smart grid, smart grid security, Smart grids, software security, Support vector machines, SVM, Training |
| Abstract | In recent years, smart grid has gradually become the common development trend of the world's power industry, and its security issues are increasingly valued by researchers. Smart grids have applied technologies such as physical control, data encryption, and authentication to improve their security, but there is still a lack of timely and effective detection methods to prevent the grid from being threatened by malicious intrusions. Aiming at this problem, a model based on machine learning to detect smart grid DoS attacks has been proposed. The model first collects network data, secondly selects features and uses PCA for data dimensionality reduction, and finally uses SVM algorithm for abnormality detection. By testing the SVM, Decision Tree and Naive Bayesian Network classification algorithms on the KDD99 dataset, it is found that the SVM model works best. |
| DOI | 10.1109/ICPICS50287.2020.9202401 |
| Citation Key | zhe_dos_2020 |
DoS attack detection model of smart grid based on machine learning method