Predictive Analysis of Zero-Day Vulnerabilities Using Deep Neural Networks

Staying one step ahead of developing threats to national security requires rapid innovation in predictive analytic approaches. This is because it is projected that the complexity of cyberthreats will continue to rise in the coming years. This paper provides a unique technique for zero-day vulnerability prediction analysis using deep neural networks (DNN). This approach was developed thanks to the characteristics of DNN. The use of historical data on anticipated weak points is at the core of our method, which was developed with the goal of providing a proactive reaction to the issues presented by elusive threats. This strategy's major purpose was to provide a proactive reaction. The recommended technique provides an all-encompassing approach and is made up of three reliable components. Convolutional neural networks (CNNs)are used for feature extraction, LSTM networks for temporal analysis, and Random Forests, or RFs, for group learning. This method, which draws on a wide variety of academic fields, provides for a complete and trustworthy prognostic analysis. These parameters include, in addition to accuracy and precision, false positive rate (FPR), mean time to detection (MTTD), area under the receiver operating characteristic curve (AUC), recall, and F1 score.