基于Transformer神经网络模型的网络入侵检测方法

网络入侵检测一直以来都是网络安全中亟待解决的关键任务之一,传统网络入侵检测方法主要通过提取多维特征,采用机器学习方法构建检测模型,大多忽略了入侵行为的时间相关性.通过提取网络入侵行为的时序特征,设计基于降维特征的多头自注意力机制Transformer网络模型,以解决传统串行化时序神经网络模型不易收敛且时间开销较大的问题,通过选取最优的损失函数和训练参数进行并行化训练,实现网络入侵行为检测.实验结果表明,基于Transformer网络模型的网络入侵检测方法在多个数据集上均获得了99％以上的精度和检出率.

Network intrusion detection method based on Transformer neural network model

Network intrusion detection has always been one of the key tasks in network security. Traditional network intrusion detection methods mainly use machine learning method to construct detection models by extracting multi-dimensional features, while most of them ignore the time correlation of intrusion behaviors. In this paper, a Transformer network model with multi-head self-attention mechanism based on dimension reduction feature was designed by extracting the time sequence features of network intrusion behavior. The proposed model solved the problems that traditional serial sequential neural network models are difficult to converge and have a large time consumption. The optimal loss function and training parameters were selected to implement the network intrusion detection. The experimental results show that the network intrusion detection method based on Transformer network model achieves the accuracy and the detection rate of over 99% in multiple datasets.