Cybercrime Investigation and Prosecution in Nigeria: Bridging the Gaps
Bibliographic record
Abstract
Abstract The investigatory framework of cybercrime is as essential as the legal and institutional framework governing cybercrime. This article argues that an effective investigation process is fundamental to the effective prosecution of cybercrime offences. Cybercrime investigation involves digital forensics, intelligence gathering, lawful interception, and use of communication data and internet networks. At its core, cybercrime investigation necessitates a comprehensive cybercrime investigation framework backed by a legal framework that ensures effective evidence collection, preservation, and analysis. This article evaluates the cybercrime investigation structure in Nigeria and highlights the gaps in Nigeria’s regulatory framework. It identifies the challenges that hinder Nigeria’s successful investigation and prosecution of cybercrime offences. The study adopts a comparative methodology by juxtaposing cybercrime investigation in Nigeria with the law and practice in the United Kingdom (UK). The UK has a robust cybercrime investigation framework, strengthened by its Cyber Security Strategy 2022. The findings show that, unlike the UK’s Regulation of Investigatory Powers Act, 2000 and the Investigatory Powers Act, 2016, the Nigerian Cybercrimes (Amendment) Act, 2024, the Administration of Criminal Justice Act, 2015 and other laws, are silent on essential investigatory initiatives, steps and specialised powers. The study proposes a practical cybercrime investigation framework to implement Nigeria’s effective prosecution of cybercrime offences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".