The Influence of Deep Learning in Detecting Cyber Attacks on E-Government Applications

Abstract

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.

Securing E-Governance Services Based on Two Level Classification Algorithms

“…Another group of studies has used deep learning approaches to detect cyber-attacks. For example, Gaur et al [19] employed this technique to classify different threats in different network areas. In this study, deep learning was employed to effectively manage a variety of cyber security issues, including intrusion detection, malware or botnet identification, phishing, forecasting of cyberattacks, denial of service (DoS), fraud detection, and cyber abnormalities.…”