IdiffGrad: A Gradient Descent Algorithm for Intrusion Detection Based on diffGrad
Weifeng Sun, Yiming Wang, Kangkang Chang, Kelong Meng · 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · 2021
Neural networks have been widely used in privacy protection and intrusion detection. As the core algorithm of neural network optimization parameters, the gradient descent algorithm is an important reason for the wide application of neural networks. For the problem that Adam may have a low learning rate in the later stage and the possibility of surpassing the global optimum, this paper proposes a new algorithm IdiffGrad based on the ratio of the first-order moment square to the second-order moment on the basis of diffGrad. The algorithm adjusts the learning rate of different dimensions based on the local variation of the gradient of this dimension and the ratio of the square of the first moment to the second moment, so as to better meet the requirement of this dimension for learning rate. Comparing IdiffGrad with Adam and diffGrad through simulation. The simulation results show that the IdiffGrad algorithm is better in comprehensive performance. It has broad prospects in various intrusion detection schemes using neural networks.