The Role of Artificial Intelligence Algorithms in Detecting Cyberattacks within E-Government Services - Farid Hasanov (Theoretical Hypotheses and Empirical Results, 2026)
The rapid and irreversible digital transformation of public administration has led to the emergence of complex E-Government ecosystems. These platforms integrate highly sensitive citizen data, financial records, and critical national infrastructure into unified digital portals. However, this centralization has introduced unprecedented cybersecurity challenges, as traditional defense mechanisms—primarily based on static signature matching—are increasingly failing to counter sophisticated, AI-driven threats, polymorphic malware, and zero-day vulnerabilities. This research provides an extensive, multi-dimensional analysis of the integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms as the primary defensive layer for E-Government services. By synthesizing empirical data and industry insights from 2023–2026 reports, including IBM X-Force, NCSC, and Google Cloud security perspectives, this study evaluates the technical efficacy of diverse algorithmic models. The research methodology involves a comparative simulation of Random Forest (RF), Support Vector Machines (SVM), and Deep Neural Networks (DNN) in detecting diverse attack vectors such as Distributed Denial of Service (DDoS), advanced Phishing, and SQL injection. The results demonstrate that deep-learning-based systems achieve a superior detection accuracy of over 97.8%, significantly reducing false positive rates and incident response times. The paper concludes with a strategic roadmap for government agencies to adopt resilient, AI-managed security frameworks while addressing the ethical, privacy, and computational challenges inherent in modern cognitive defense.