ADINT: Machine Learning-Powered Advertisement Intelligence for Proactive Threat Detection in Nigeria’s Digital Ecosystem

Muhammad Nazeer, Habib Mohammed, Nafisat Abdulkadir, Philip Oshiokhaimhele Odion, Martins Ekata Irhebhude, Saifullahi Sadi Shitu
Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, PMLR 319:406-420, 2026.

Abstract

Digital advertising platforms have become unexpected threat vectors in Nigeria: terrorist groups exploit social media ads for recruitment, cybercriminals launder funds through advertising-related fraud, and human traffickers lure victims via deceptive job postings. Yet Nigerian security agencies lack systematic capabilities to monitor advertising content as an intelligence source. This paper presents ADINT, the first machine-learning-based advertisement intelligence framework designed for Nigeria’s threat landscape. A domain-informed synthetic dataset of 3,000 advertisements across four categories—benign (54.93%), fraud (22.90%), terrorism (11.70%), and trafficking (10.47%)—incorporates realistic class imbalance, graduated ambiguity, and lexical noise to simulate operational conditions. A six-phase experimental pipeline evaluates four architectures: BERT achieves the highest accuracy (91.33%) with perfect recall on terrorism and trafficking; Random Forest (90.33%) offers a compelling efficiency-accuracy trade-off for resource-constrained deployment. A proposed two-stage cascade—Random Forest pre-filter followed by BERT refinement—is analytically projected to reduce analyst workload by 75–78% while maintaining zero false negatives on critical threat classes within the synthetic evaluation environment.

Cite this Paper

BibTeX
@InProceedings{pmlr-v319-nazeer26a, title = {{ADINT}: Machine Learning-Powered Advertisement Intelligence for Proactive Threat Detection in {Nigeria’s} Digital Ecosystem}, author = {Nazeer, Muhammad and Mohammed, Habib and Abdulkadir, Nafisat and Odion, Philip Oshiokhaimhele and Irhebhude, Martins Ekata and Shitu, Saifullahi Sadi}, booktitle = {Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments}, pages = {406--420}, year = {2026}, editor = {Folorunso, Sakinat and Ogundokun, Roseline and Oladipo, Francisca}, volume = {319}, series = {Proceedings of Machine Learning Research}, month = {11--14 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v319/main/assets/nazeer26a/nazeer26a.pdf}, url = {https://proceedings.mlr.press/v319/nazeer26a.html}, abstract = {Digital advertising platforms have become unexpected threat vectors in Nigeria: terrorist groups exploit social media ads for recruitment, cybercriminals launder funds through advertising-related fraud, and human traffickers lure victims via deceptive job postings. Yet Nigerian security agencies lack systematic capabilities to monitor advertising content as an intelligence source. This paper presents ADINT, the first machine-learning-based advertisement intelligence framework designed for Nigeria’s threat landscape. A domain-informed synthetic dataset of 3,000 advertisements across four categories—benign (54.93%), fraud (22.90%), terrorism (11.70%), and trafficking (10.47%)—incorporates realistic class imbalance, graduated ambiguity, and lexical noise to simulate operational conditions. A six-phase experimental pipeline evaluates four architectures: BERT achieves the highest accuracy (91.33%) with perfect recall on terrorism and trafficking; Random Forest (90.33%) offers a compelling efficiency-accuracy trade-off for resource-constrained deployment. A proposed two-stage cascade—Random Forest pre-filter followed by BERT refinement—is analytically projected to reduce analyst workload by 75–78% while maintaining zero false negatives on critical threat classes within the synthetic evaluation environment.} }
Endnote
%0 Conference Paper %T ADINT: Machine Learning-Powered Advertisement Intelligence for Proactive Threat Detection in Nigeria’s Digital Ecosystem %A Muhammad Nazeer %A Habib Mohammed %A Nafisat Abdulkadir %A Philip Oshiokhaimhele Odion %A Martins Ekata Irhebhude %A Saifullahi Sadi Shitu %B Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments %C Proceedings of Machine Learning Research %D 2026 %E Sakinat Folorunso %E Roseline Ogundokun %E Francisca Oladipo %F pmlr-v319-nazeer26a %I PMLR %P 406--420 %U https://proceedings.mlr.press/v319/nazeer26a.html %V 319 %X Digital advertising platforms have become unexpected threat vectors in Nigeria: terrorist groups exploit social media ads for recruitment, cybercriminals launder funds through advertising-related fraud, and human traffickers lure victims via deceptive job postings. Yet Nigerian security agencies lack systematic capabilities to monitor advertising content as an intelligence source. This paper presents ADINT, the first machine-learning-based advertisement intelligence framework designed for Nigeria’s threat landscape. A domain-informed synthetic dataset of 3,000 advertisements across four categories—benign (54.93%), fraud (22.90%), terrorism (11.70%), and trafficking (10.47%)—incorporates realistic class imbalance, graduated ambiguity, and lexical noise to simulate operational conditions. A six-phase experimental pipeline evaluates four architectures: BERT achieves the highest accuracy (91.33%) with perfect recall on terrorism and trafficking; Random Forest (90.33%) offers a compelling efficiency-accuracy trade-off for resource-constrained deployment. A proposed two-stage cascade—Random Forest pre-filter followed by BERT refinement—is analytically projected to reduce analyst workload by 75–78% while maintaining zero false negatives on critical threat classes within the synthetic evaluation environment.
APA
Nazeer, M., Mohammed, H., Abdulkadir, N., Odion, P.O., Irhebhude, M.E. & Shitu, S.S.. (2026). ADINT: Machine Learning-Powered Advertisement Intelligence for Proactive Threat Detection in Nigeria’s Digital Ecosystem. Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, in Proceedings of Machine Learning Research 319:406-420 Available from https://proceedings.mlr.press/v319/nazeer26a.html.

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