Autonomous Artificial Intelligence Systems for Fraud Detection and Forensics in Dark Web Environments

Authors

  • Romil Rawat Department of Computer Architecture and Communications , University of Extremadura, 06006, Badajoz, Spain
  • Olukayode Oki Department of Information Technology, East London, Walter Sisulu University, South Africa
  • Rajesh Kumar Chakrawarti Department of Computer Science and Engineering, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, India
  • Temitope Samson Adekunle
  • Jose Manappattukunnel Lukose Department of Information Technology, Walter Sisulu University, South Africa
  • Sunday Adeola Ajagbe Department of Computer Engineering, Ladoke Akintola University of Technology, LAUTECH, Ogbomoso, Nigeria

DOI:

https://doi.org/10.31449/inf.v46i9.4538

Abstract

Artificial Intelligence (AI) influenced technical aspects of research for generating automated intelligent behaviors covering divergent domains but has shown appreciable results when used in cyber forensic technology for crime analysis and detection. AI experts warned about possible security risks associated with algorithms and training data, as AI inherits computing features dealing with IoT-based Smart applications and autonomous transportation, and may be found to be susceptible to vulnerability and threats. The present work discusses models for analyzing terrorists related information and categorizing malicious events by covering the literature review on security risks and AI-related criminality by presenting a taxonomy of criminal behavior and signatures covering tools and criminal targets used in fraudulent activities using AI features by digital forensic techniques. We’ve also shown how AI may make existing crimes more potent, and that new sorts of crimes could emerge that haven't been identified previously. This study has presented a systematic structure for AI crime and dealing strategies. Furthermore, we have proposed AI forensics, a unique strategy for combating AI crime. We discovered that several concepts of DF are still not preferred in AI-based forensics after conducting a comparative examination of forensics. 

Author Biography

Sunday Adeola Ajagbe, Department of Computer Engineering, Ladoke Akintola University of Technology, LAUTECH, Ogbomoso, Nigeria

Sunday Adeola Ajagbe is a PhD candidate of Computer Engineering at Ladoke Akintola University of Technology (LAUTECH), Ogbomoso, Nigeria, he obtained MSc and BSc in Information Technology and Communication Technology respectively at the National Open University of Nigeria (NOUN), and his Postgraduate Diploma in Electronics and Electrical Engineering at LAUTECH, and Higher National Diploma in Electrical and Electronics Engineering, The Polytechnic, Ibadan. His specialization includes Natural language processing, Signal processing, Security, Quantum computing, Data Science, Artificial Intelligence, Deep Learning, and Machine Learning, Smart solution, and biomedical engineering. He has successfully carried out researches and published many articles. He is a member of many academic/professional bodies including Nigeria Computer Society among others.

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Published

2023-10-26

How to Cite

Rawat, R., Oki, O., Chakrawarti, R. K., Adekunle, T. S., Lukose, J. M., & Ajagbe, S. A. (2023). Autonomous Artificial Intelligence Systems for Fraud Detection and Forensics in Dark Web Environments. Informatica, 47(9). https://doi.org/10.31449/inf.v46i9.4538

Issue

Vol. 47 No. 9 (2023): Online-only issue

Section

Online-only

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