Deep Learning-Based Methodology for Tracking Cybersecurity in Networked Computers

Introduction

Currently, the threat environment is undergoing significant changes, leading to an increase in cybersecurity events involving computer networks (CN). Documented CN cybersecurity (Bansal, R. et al. 2022) events include malware infections, denial of service (DoS) (Pradhan, D. et al. 2022) assaults, and phishing (Dushyant, K. et al. 2022) attacks, Man-in-the-middle (MitM) (Kaushik, D. et al. 2022) attacks, SQL injection attacks (Gupta, A. et al. 2022), network scanning and probing, password cracking, and unauthorized access. Some potential risks to consider include unauthorized access, data breaches, insider threats, system misconfigurations, and violations of security policies. These occurrences may result in significant ramifications may occur, such as the unauthorized disclosure of personal data, the interruption of network functionality, and the impairment of network applications and services. Effective surveillance of cybersecurity is an essential component in safeguarding the security of computer networks. This process entails ongoing surveillance and examination, and defense against cyber attacks. Nevertheless, the task of monitoring computer cybersecurity has evolved into a very intricate endeavor job involving the growing size, intricacy, and continuously growing interconnection of contemporary computer systems and the amount of data created inside them. This is mostly due to the inherent limitations of conventional cybersecurity monitoring techniques effectively and expediently processing a substantial amount of data in real-time. Hence, sophisticated cybersecurity surveillance Deep learning (Chandan, R. R. et al. 2023) is an essential element of the CN cybersecurity system.

Conventionally, a single deep learning model is used to analyze the whole dataset, yielding excellent results particularly in cases when there is a substantial volume of data. Nevertheless, when dealing with a limited dataset, a single deep learning model may encounter difficulties. When using a single DL model to address the issue at hand is to the surveillance of the cybersecurity of a computer network (CN). Specifically, there may be a challenge in obtaining various forms of data. Cybersecurity incident data encompasses several variables, such as location and temporal characteristics, which aid in comprehending the subject matter. The cybersecurity event patterns exhibit an intricate dispersion of data. This is due to the inability of current deep learning models to concurrently extract several sorts of characteristics from CN cybersecurity incident data and may not be efficient in detecting distinctive designs. Consequently, current deep learning models are unable to comprehensively analyze the diverse characteristics of these Chinese cybersecurity events Isolate the data. Hence, this study suggests using a combination of several deep learning models for the purpose of monitoring cybersecurity in computer networks. Analyze various attributes from CN cybersecurity incident data. Simultaneously, every DL model focuses on remedying a certain condition. Implementing such an approach may greatly enhance the efficacy of cybersecurity surveillance for the CN. This chapter aims to create a technique for overseeing the security of CN by using a combination of DL models. In this scenario, a convolutional neural network (CNN) (Meslie, Y. et al. 2021) and a recurrent neural network with long short-term memory (LSTM) (Ahamad, S. et al. 2023) are used. By using these models in conjunction, we may effectively extract diverse categories of information from cybersecurity event data. This will enhance the precision of categorizing and forecasting cybersecurity occurrences. Simultaneously, CNN permits the process of extracting local patterns from the data involves identifying recurring patterns within a certain region or area. On the other hand, LSTM (Long Short-Term Memory) is a kind of neural network that is capable of analyzing and understanding the temporal relationships and dependencies present in the data. The subsequent sections of the paper are structured in the following manner. Section 2 provides an analysis of the works that are relevant to the topic. Section 3 provides information about cybersecurity that is based on deep learning. The CN cybersecurity monitoring approach based on CNN-LSTM is introduced in Section 4. Section 5 provides a detailed description of the dataset and experimental setup used for training the proposed model. Section 6 provides an exposition of the experimental findings and subsequent analysis. Ultimately, the conclusion delineates the concepts for next study endeavors.