The digitalization revolution plays a crucial role in every government administration. It manages a considerable volume of user information and is currently seeing an increase in internet access. The absence of unorganized information, on the other hand, adds to the difficulty of data analysis. Data mining approaches have recently become more popular for addressing a variety of e-governance concerns, particularly data management, data processing, and so on. This chapter identifies and compares several existing data mining and data warehouses in e-government. Deep learning is a subset of a larger class of machine learning techniques that combine artificial neural networks. The significance and difficulties of e-governance are highlighted for future enhancement. As a result, with the growth of e-governance, risk and cyber-attacks have increased these days. Furthermore, the few e-governance application performance evaluations are included in this chapter. The purpose of this chapter is to focus on deep learning applications of e-governance in detecting cyber-attacks.
Top1. Introduction
E-Government is the result of governments' efforts to enhance and improve their relationships with their citizens. If certain requirements are met, the legitimate estimation of electronic exchanges will be comparable to the legitimate estimation of other types of correspondence, such as written structure. Very effective and feasible data security practices and prevention measures are required to ensure the success of e-government ventures. Security rules, methods, and techniques must be established, as well as the use of security innovation, which helps to secure e-government frameworks against attack (Shorten et al., 2021) Distinguish odd exercises from administrations, and have a documented emergency course of action built up. Essential parts include having a legal public key foundation that provides the appropriate level of verification and integrity, as well as having ongoing awareness and planning a project to ensure individuals comprehend security risks, understand how to spot any concerns, and act as needed to keep up a safe e-government administration. Residents' desire for efficient and cost-effective governments is boosting the growth of e-government projects (Sit et al., 2020). The scope of e-government control and its impact on a network characterize a framework that is more than just a single framework. A network-based digital security approach is required to investigate security issues across the entire framework. The outcomes of recent network-based activities have provided insight into potential opportunities for advancement and have demonstrated the estimation of these events. Their valuables pose a serious threat to public safety, national security, and the overall strength of the globally connected universal network. It can be difficult to identify the source, attacker’s origin, and behavior, or the motivation for the disruption (Kuutti et al., 2020). Criminals must frequently be deduced from the objective, the impact, or other unintentional evidence. On-screen actors who are dangerous can work from almost anywhere. Displaying specialized talent to stealing information. Hackers and programmers are responsible for a wide range of harmful gadgets and systems. The growing complexity and scale of crime raise the risk of destructive behavior. E-government is the result of legislators' efforts to strengthen relations with their constituents. Given the Internet's norms, e-governance brings governments even closer to their citizens (Zhang et al., 2019). As a result, e-government has a greater societal advantage because it ensures a more extensive and agent-majority rules system. The ability to respond to changing conditions due to the constant ageing and exploitation of new information is crucial in an information economy. Many businesses are unable to function without the use of ICT in their daily operations. Deep Learning has had a sign of success with “Natural Language Processing” (NLP). Literature mining, misinformation detection, and public sentiment analysis are some of the applications for COVID-19 (Lee et al., 2019). Deep learning (DL) is a subset of machine learning (ML) and artificial intelligence (AI) and is based on an artificial neural network (ANN). It is one of the primary technologies of the Fourth Industrial Revolution (Thabit & Jasim, 2019). “Cyber security” and “Deep learning” are becoming increasingly popular around the world. Deep neural network learning techniques, as well as their ensembles and hybrid approaches, can be used to intelligently handle various cybersecurity concerns, such as intrusion detection, malware or botnet identification, phishing, forecasting cyber-attacks, such as denial of service (DoS), fraud detection, or cyber-anomalies figure 1 Overview of Deep Learning for security intrusion detection . Deep learning has advantages in terms of building security models because it is more accurate, especially when learning from massive amounts of security datasets.
Figure 1. Overview of Deep Learning for security intrusion detection (Gupta, Pal, & Muttoo, 2020)
The following points are discussed in this chapter: