The Role of Artificial Intelligence in Strengthening Privacy and Security in the Era of Cyber Crime and Digital Forensics

Victoria Abosede Ogunsanya, Rianat Abbas, Laticbe Elijah, Joy Awoleye, Adetomiwa Adesokan, Kumbirai Bernard Muhwati, & Average Guma
Cybersecurity Analyst, University of Bradford, UK
Information Systems, Baylor University, Texas, USA
Cybersecurity, Yeshiva University, New York, USA
Cybersecurity, Yeshiva University, New York, USA
Computer Science, Yeshiva University, New York, USA
Cybersecurity, Yeshiva University, New York, USA
DOI – http://doi.org/10.37502/IJSMR.2025.8515

Abstract

This study explores the role of Artificial Intelligence (AI) in strengthening privacy and security amidst the growing challenges of cybercrime and digital forensics. As cyber-attacks become more sophisticated, traditional methods of securing sensitive data and investigating digital crimes are increasingly inadequate. This research examines the use of machine learning algorithms, particularly Random Forest Classifier (RFC) and Gradient Boosting Classifier (GBC), in detecting network anomalies and enhancing the detection of cyberattacks. The study also highlights the critical need for AI-driven techniques to support digital forensic investigations by providing more accurate and efficient methods of identifying malicious activities. Using a dataset of network traffic features, the study reveals the class imbalance between normal and attack traffic, which can hinder detection accuracy. Despite this imbalance, both RFC and GBC achieved perfect classification with AUC scores of 1.00. GBC, however, outperformed RFC in accuracy (91.3%), precision (91.4%), and recall (91.3%), demonstrating its superior ability to identify attack traffic while preserving privacy. Feature importance analysis found that Average Packet Size and Fwd Packets Length were the most significant indicators of attack behavior. The findings underscore the importance of AI in enhancing cybersecurity systems, ensuring robust privacy protections, and advancing digital forensic capabilities. The study also emphasizes the need for continuous model retraining, class balancing, and hyperparameter tuning to adapt to evolving threats. These AI-driven approaches have the potential to transform the landscape of digital forensics and cybersecurity, offering more resilient defenses against cybercrime and safeguarding privacy in an increasingly digital world.

Keywords: Artificial Intelligence, Cybercrime, Digital Forensics, Privacy Protection, Machine Learning, Random Forest Classifier, Gradient Boosting Classifier, Cybersecurity, Attack Detection, Feature Importance, Model Evaluation

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