Artificial Intelligence Based Anomaly Detection For Secure E-Government Transaction
Keywords:
e-Government, Artificial Intelligence, Anomaly Detection, Cybersecurity, BlockchainAbstract
Artificial Intelligence (AI)-based anomaly detection has emerged as a critical technological enabler for strengthening the security and reliability of e-government transactions in the digital era. As public services increasingly migrate to online platforms, the volume and complexity of transactional data have grown exponentially, creating heightened vulnerabilities to cyberattacks, fraud, identity theft, and unauthorized access. This study explores the integration of AI-driven anomaly detection systems—including machine learning, deep learning, and hybrid analytical models—to identify irregular behavioral patterns, detect malicious activities in real time, and minimize false positives that traditionally challenge rule-based systems. The research underscores the significance of automated feature learning, behavioural profiling, and predictive analytics in securing sensitive governmental datasets and ensuring uninterrupted service delivery. Furthermore, it examines the applicability of models such as isolation forests, autoencoders, and recurrent neural networks (RNNs) for detecting subtle deviations within high-dimensional e-government data streams. The paper also discusses architectural considerations, challenges related to data privacy, scalability, and integration with existing government infrastructures. Findings indicate that AI-powered anomaly detection enhances operational transparency, strengthens trust in digital governance, and enables proactive defense mechanisms against emerging threats. Overall, this work contributes a comprehensive framework for adopting intelligent security solutions to safeguard modern e-government ecosystems.
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