DASN: A Decentralized Anonymous Framework for Community Intelligence and Criminal Logistics Disruption in Nigeria

Oluwaseun Adeniyi Ojerinde; Noibi Kolawole AbdulSalaam; Ramatu Abubakar; Joseph Adebayo Ojeniyi

doi:10.56427/jcbd.v5i3.1058

Authors

Oluwaseun Adeniyi Ojerinde Federal University of Technology Minna
AbdulSalaam Kolawole Noibi Federal University Of Technology, minna, niger state
Ramatu Abubakar Federal University Of Technology, minna, niger state https://orcid.org/0009-0003-8384-4646
Joseph Adebayo Ojeniyi Federal University Of Technology, minna, niger state

DOI:

https://doi.org/10.56427/jcbd.v5i3.1058

Keywords:

Natural Language Processing, Smart Contracts, Community Intelligence, Criminal Logistics Disruption, Predictive Policing

Abstract

Organized banditry and mass kidnapping in Nigeria depend on physical logistics, such as fuel, ammunition, food and communication credit, that reactive security responses largely overlook. Community members can observe these logistics anomalies, but reporting is constrained by fear of retaliation, identity exposure and the lack of automated means to separate credible reports from decoys. This paper proposes the Decentralized Anonymous Security Network (DASN), a proof-of-concept framework that combines a machine-learning and rule-based decision layer with an Ethereum-compatible blockchain ledger. DASN accepts reports through Unstructured Supplementary Service Data (USSD) and a Progressive Web Application and replaces the informant's telecommunications identifier with a salted hash at ingestion. A spaCy Named Entity Recognition component and a Random Forest classifier estimate threat probability, while a Solidity smart contract anchors report hashes on-chain and maintains informant reputation scores. On synthetic evaluation data, Random Forest achieved the highest performance among four classifiers, with 85% accuracy, 0.86 precision, 0.85 recall and 0.85 F1-score. Hybrid on-chain/off-chain storage reduced gas consumption by approximately 70%, and asynchronous processing returned user acknowledgements in approximately 50 milliseconds. The results show the technical feasibility of integrating low-bandwidth reporting, informant pseudonymity, reputation scoring and criminal-logistics analytics in a single architecture.

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