Biblio

Anomaly detection is the primary method of detecting intrusion. Unsupervised models, such as auto-encoders network, auto-encoder, and GMM, are currently the most widely used anomaly detection techniques. In reality, the samples used to train the unsupervised model may not be pure enough and may include some abnormal samples. However, the classification effect is poor since these approaches do not completely understand the association between reconstruction errors, reconstruction characteristics, and irregular sample density distribution. This paper proposes a novel intrusion detection system architecture that includes data collection, processing, and feature extraction by integrating data reconstruction features, reconstruction errors, auto-encoder parameters, and GMM. Our system outperforms other unsupervised learning-based detection approaches in terms of accuracy, recall, F1-score, and other assessment metrics after training and testing on multiple intrusion detection data sets.

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.