Artificial Intelligence and Machine Learning for Predictive Threat Intelligence in Government Networks

Abstract

In today's interconnected digital landscape, government networks face increasingly sophisticated cyber threats that demand proactive defense mechanisms. This paper explores the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in enhancing predictive threat intelligence capabilities within government networks. By leveraging AI and ML algorithms, governments can analyze vast amounts of heterogeneous data sources to identify potential threats, predict future cyber attacks, and strengthen preemptive security measures. This research investigates various AI and ML models such as supervised and unsupervised learning, anomaly detection, natural language processing (NLP), and deep learning to illustrate their efficacy in predicting and mitigating cyber threats. Case studies and real-world examples demonstrate the practical implementation and benefits of these technologies in enhancing cybersecurity posture and safeguarding sensitive government information.