Hybrid AI and Lightweight Cryptography Framework for Proactive Threat Intelligence and Secure Critical Infrastructure in Nigeria

As cyberattacks grow in complexity and resource-constrained systems become increasingly vulnerable, protecting Nigeria's Critical National Infrastructure (CNI) has become an urgent national security priority. Current defenses are predominantly reactive, exposing key sectors such as energy, transportation, healthcare, and finance to evolving threats. This study proposes a hybrid framework that integrates lightweight cryptography (LWC) with artificial intelligence (AI) to proactively identify and mitigate cyber threats against Nigeria's CNI. Following a design science research (DSR) approach, the framework draws on multiple datasets including NASRDA satellite data, Twitter-sourced OSINT, the CVE database, the CSEAN threat index, Nigerian Bureau of Statistics sectoral data, and Kaggle cybersecurity datasets. Results demonstrated AI model performance exceeding F1-score of 0.92, precision of 0.94, and recall of 0.90, with a false positive rate below 5% and inference latency under 2 seconds. The NIST-standardised ASCON cipher achieved approximately 78% energy savings over AES-128-GCM with 2.4× higher encryption throughput. Integrated simulation reduced attack success rates from above 70% to below 20%. This work provided a scalable, cost-effective, and contextually relevant security framework applicable to other developing nations facing similar infrastructure and cybersecurity challenges. Keywords: Artificial Intelligence; Cryptography; Cybersecurity; Critical Infrastructure; CNI, Nigeria, Threat Intelligence; Nigeria. Kile, S.A. & Adoga, P.I. (2026): Hybrid AI and Lightweight Cryptography Framework for Proactive Threat Intelligence and Secure Critical Infrastructure in Nigeria. Journal of Advances in Mathematical & Computational Science. Vol. 14, No. 2. Pp 9-28. Available online at isteams.net. dx.doi.org