Intelligent detection of network security vulnerabilities based on machine learning and penetration behavior analysis

With the increasing frequency of network attacks, the traditional methods of network vulnerability detection are faced with many challenges. Within this manuscript, a network vulnerability detection framework, utilizing a deep neural network (DNN) that amalgamates advanced deep learning techniques with conventional machine learning methodologies, is posited to enhance discernment capabilities when pinpointing potential threats within network data streams. Through the nonlinear combination of multi-layer neurons, the model can effectively capture complex feature relationships, so as to achieve accurate classification of normal traffic and attack traffic. The model's structure encompasses an input stratum, manifold concealed strata, and an outer stratum. The ReLU activation function is harnessed to amplify the model's nonlinearity, incorporating the Sigmoid activation function in the latest layer augments the process of binary categorization. The choice of binary cross entropy as the loss function is made to refine the model's performance. The experimental blueprint ensures data integrity and dependability via the procedural measures of data prepossessing, feature culling, and normalization. In the training process, the weight of the model is constantly updated by backpropagation algorithm to improve its generalization ability on unknown data.