Authors : Isaac Kwame Antwi; Eric Akwei; Olanrewaju Ogundojutimi; Nicholas Donkor

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/5fyzw268

DOI : https://doi.org/10.38124/ijisrt/25may2294

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This paper presents an AI-driven infrastructure protection framework to enhance the resilience of enterprise networks. It integrates machine learning, threat intelligence, and cloud-native orchestration to detect threats, profile behaviors, and automate responses. The architecture ingests network logs and telemetry, applies anomaly detection and risk scoring, and correlates results with threat intelligence for real-time policy enforcement. Evaluation using CICIDS 2017 & 2020 datasets shows the framework outperforms traditional intrusion detection systems in accuracy and responsiveness. LSTM and Random Forest models achieved the best results, confirmed through ROC and confusion matrix analysis. Feature importance insights and a dynamic risk scoring engine support scalable and context-aware decision-making. This work demonstrates the effectiveness of combining AI with cloud-native defense for proactive, intelligent cybersecurity. Future extensions will explore explainable AI, federated learning, and adversarial robustness.

Keywords : AI-Driven Cybersecurity, Enterprise Network Protection, Anomaly Detection, Threat Intelligence Correlation, Cloud- Native Defense.

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