Identification of Cybersecurity Elements Based on Convolutional Attention LSTM Networks

Abstract

As the first step of cybersecurity situational awareness, the accuracy of cybersecurity element recognition will directly affect the results of situational understanding and situational prediction. In this paper, we propose a network element recognition method based on the convolutional attention mechanism combined with a long- and short-term memory network. The input network traffic data is successively passed through the convolutional neural network, attention mechanism, and long- and short-term memory network, which not only takes into account the influence degree of different network attributes on different network behaviors but also realizes that the feature information extracted in the early stage can be circulated in the network, thus providing a discriminant basis for the final network behaviors To verify the effectiveness of our proposed method, we perform experimental validation on the KDD-Cup 1999 (kdd-99) dataset. The results show that our proposed method achieves an accuracy of 98.48% in the identification of network security elements. In addition to this, we also compare and analyze our proposed algorithm with other mainstream algorithms, and the results also validate the effectiveness of our proposed method.