Deep Learning-Based Methodology for Tracking Cybersecurity in Networked Computers

Abstract

Effective surveillance of cybersecurity is essential for safeguarding the security of computer networks. Nevertheless, due to the increasing scope, complexity, and amount of data created by computer networks, cybersecurity monitoring has become a more intricate issue. The difficulty of correctly and effectively monitoring computer network cybersecurity is a challenge faced by traditional approaches examining a greater quantity of data. Hence, using deep learning models to oversee computer network cybersecurity becomes necessary. This chapter introduces a technique for overseeing the cybersecurity of computer networks by using deep learning knowledge about models. The combination of CNN (convolutional neural networks) and LSTM (long short-term memory) models is used for monitoring the cybersecurity of computer networks. This combination enhances the accuracy of classifying network cybersecurity problems. The CICIDS2017 dataset is used for training and evaluating the suggested model.