Abstract: Conventional intrusion detection methodologies based on deep learning necessitate a substantial number of labeled samples to attain optimal accuracy.Nevertheless,the acquisition of a substantial number of labeled samples necessitates a considerable investment of time and labor,which constrains its applicability in practical settings.In order to address these limitations,a novel intrusion detection method that integrates active learning with convolutional neural networks is proposed.This method employs an enhanced adaptive active learning approach to more efficiently identify the most representative samples for labeling,effectively reducing the computational cost of the model training process and enhancing the overall performance of the model.The experimental results on the CCF-BDCI-2022 and Malicious-URLs-2021 datasets demonstrate that the proposed method exhibits superior performance in terms of query time and iteration time compared to traditional deep learning-based models.In the CCF-BDCI-2022 dataset,the method demonstrates an accuracy rate of 97.10% and a false positive rate of 1.3%.In the Malicious-URLs-2021 dataset,the method achieves an accuracy rate of 99.05% and a false positive rate of 1.1%.Compared with other methods,this method not only performs better in terms of accuracy and false positive rate,but also significantly reduces the consumption of computing resources,thereby improving the efficiency and practicality of the model.

Key words: Active learning,Intrusion detection,Convolutional neural network,K-means,Sample annotation