Network security situation assessment based on dual attention mechanism and HHO-ResNeXt

Abstract

The traditional convolutional neural network (CNN) has a limited receptive field and cannot accurately identify the importance of each channel, making it difficult to solve increasingly complex network security problems. To solve these problems, this paper combines ResNeXt with the Efficient Channel Attention (ECA) module and the Contextual Transformer (COT) block to construct a model to assess network conditions. The optimal hyperparameters of the model are selected by the Harris Hawks Optimization (HHO) algorithm. The model can accurately obtain the importance of each channel to assign weights to each channel while making full use of the rich contexts among neighbour keys, effectively enhancing the convolutional neural network. Furthermore, this paper calculates the network security situation value (NSSV) of the adopted datasets based on attack impact. Lastly, experiments on two cybersecurity datasets show that the comprehensive performance of the model on the three indicators of accuracy, precision and F-scores, as well as network security situation assessment, are superior to other models.

KEYWORDS:

• Efficient channel attention module
• contextual transformer block
• ResNeXt
• Harris hawks optimisation
• network security situation assessment

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 61672206]; the Hebei Science Supported Planning Projects Under Grant 20310701D & 22567606H & 216Z0701G.