中圖分類號：TP393.08;TP18
文獻標識碼：A
DOI:10.19358/j.issn.2097-1788.2023.07.008
引用格式：王明.基于元學習的多頭注意力時序卷積的入侵檢測［J］.網絡安全與數(shù)據(jù)治理，2023,，42（7）：49-54.

Intrusion detection based on meta-learning with multi-head attention temporal convolution

Abstract： To address the performance limitations of existing intrusion detection methods in mining highorder dependency relationships, processing temporal features, and detecting new types of attack methods, this paper proposes an intrusion detection method based on metalearning and multihead attention temporal convolution. This method introduces a multihead attention mechanism, allowing the model to capture the temporal features and highorder dependency relationships of network data at different scales. Secondly, this paper combines multitask learning to improve the metalearning algorithm for identifying unknown network attacks, thus enhancing the detection performance of unknown network attacks. In addition, this paper designs an adaptive feature extraction strategy that dynamically adjusts the feature extraction granularity to adapt to different types of network attacks. Experimental comparisons on public datasets show that the proposed algorithm has higher accuracy and Fscore compared to mainstream algorithms.

Key words : intrusion detection,； metaLearning ；multihead attention mechanism; temporal convolutional neural network