Artificial Intelligence Applications for Enhancing Security and Threat Detection in Northeast Nigeria

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@article{1723634,
      author = {Ibrahim Musa Ibrahim, Ahmad Abdulkareem, Mohammed Wakilbe},
      title = {Artificial Intelligence Applications for Enhancing Security and Threat Detection in Northeast Nigeria},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {178-194},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723634.pdf},
      abstract = {The security landscape of Northeast Nigeria has, for over a decade and a half, been shaped by the protracted Boko Haram and Islamic State West Africa Province (ISWAP) insurgency, whose mass-casualty attacks, large-scale abductions and cross-border operations across the Lake Chad basin have produced one of the world's largest internally displaced populations. Conventional security architectures, reliant on manual intelligence gathering, static checkpoints and after-the-fact investigation, have struggled to keep pace with the speed, mobility and adaptive tactics of these threat actors. This paper presents a comprehensive review and conceptual framework for the application of Artificial Intelligence (AI) — spanning machine learning, deep learning, natural language processing, computer vision, and predictive geospatial analytics — to security and threat detection in Northeast Nigeria. The paper synthesizes evidence from the global and Nigeria-specific literature on AI-enabled surveillance, biometric identification, unmanned aerial vehicle (UAV) reconnaissance, social-media-based extremism monitoring, financial-crime detection, and predictive crime mapping, and proposes a layered architecture that integrates these techniques into a coherent threat-detection pipeline suited to a low-connectivity, resource-constrained operating environment. The discussion further examines the practical, infrastructural, and ethical constraints that condition AI adoption in the region — including unreliable electricity supply, data scarcity, algorithmic bias, and privacy risk — and closes with policy-oriented recommendations for security agencies, technology developers, and government institutions. The paper argues that AI should be understood not as a stand-alone solution but as a force multiplier that augments human intelligence and community policing, provided that deployment is grounded in transparency, local data governance, and human rights safeguards.},
      keywords = {Artificial intelligence, Boko Haram, insurgency, machine learning, national security, Northeast Nigeria, predictive policing, threat detection.},
      month = {October},
  }