AI-Driven System for Profiling Cybercrime Tendencies: A Preventive Approach
This paper presents a novel system for predicting cybercrime tendencies using machine learning and behavioral analysis, based on datasets from surveys covering opinions, online activities, and socio-demographic factors. The system aims to identify patterns linked to cybercriminal behavior, providing actionable insights for student counselors, law enforcement, and cybersecurity professionals to enable proactive interventions and targeted prevention efforts, particularly within tertiary institutions.
Authors
- Greg Onwodi (ORCID: https://orcid.org/0000-0003-0639-5376)
- Wilson Nwankwo (ORCID: https://orcid.org/0000-0003-1655-1440)
- Chukwuemeka Pascal Nwankwo (ORCID: https://orcid.org/0009-0001-9967-2856)
- Duke Oghorodi (ORCID: https://orcid.org/0009-0000-9497-1016)
- Kingsley Ukaoha (ORCID: https://orcid.org/0000-0002-6147-2314)
- Emmanuel Ojei
- Edwin Onatuyeh (ORCID: https://orcid.org/0000-0001-5252-8681)
Institutions
- Delta State University (NG)
- Delta State Polytechnic Ogwashi-Uku (NG)
- National Open University of Nigeria (NG)
Publication Details
- Published
- 2024-11-26
- DOI
- https://doi.org/10.1109/nigercon62786.2024.10927117
- Primary Topic
- Advanced Malware Detection Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00