Semi-supervised learning method based on Fuzzy-LSTM for intrusion detection

Abstract

The existing supervised learning methods can only use labelled samples to train the classifier, which is difficult and costly to obtain labels. To solve the problem and enhance the effectiveness of intrusion detection models, a semi-supervised learning method is proposed in this study in terms of intrusion detection based on Fuzzy-Long Short-Term Memory (Fuzzy-LSTM). The model uses long short-term memory to generate labels for unlabelled samples, while classifying samples based on fuzzy entropy. The low fuzzy entropy samples from them were merged into the original training set, and the classifier was trained again. The results showed that the proposed model had the accuracy of 84.53% for the above data sets, 2.45% higher than that of the classical CNN-BiLSTM, respectively, and the improvement of the detection accuracy for a few classes of samples was significant.