Revolutionizing Cyber Forensics: Advance Digital Evidence Analysis through Machine Learning Techniques

Abstract

The exponential growth in cybercrimes has turned digital forensics into a foundation of contemporary cybersecurity. Conventional forensic tools are not scalable, accurate and efficient particularly when dealing with large and diverse data sources. This study investigates cutting-edge machine learning (ML) techniques to improve digital evidence collection, examination and attribution in cyber forensic investigations. We suggest an end-to-end ML-based framework incorporating Natural Language Processing (NLP), deep neural networks and ensemble learning algorithms to classify evidence automatically identify anomalies and profile suspects. It includes transformer-based models for text analysis, Convolutional Neural Networks (CNNs) for image forensics, autoencoders for anomaly detection and ensemble models for event correlation and suspect profiling. Large-scale experimentation was carried out using real-world forensic datasets such as system logs, network traffic captures, social media posts, email archives, images and videos. Preprocessing techniques involved noise reduction, normalization, NLP tokenization and image augmentation to maximize model performance. Experimental evidence shows that the ML model attained a 94.3% accuracy in digital evidence categorization 92.7% precision in network anomaly identification and 95.1% accuracy in email threat assessment. Compared to traditional techniques, the suggested system saved 57% forensic analysis time, highlighting its efficiency and dependability. The paper also examines challenges like small forensic datasets, model interpretability problems, adversarial ML threats and legal admissibility issues. Future research areas encompass the incorporation of Explainable AI (XAI) for transparency, creating adversarial-resistant models and engaging legal experts in ensuring forensic systems conform to judicial norms. The results highlight the revolutionary capability of intelligent machine learning models to create proactive, scalable and consistent digital forensic frameworks, setting the stage for future generations of cybercrime investigations.

Key words: Cyber Forensics, Machine Learning, Digital Evidence, Deep Learning, NLP, Anomaly Detection, AI in Forensics, Adversarial Robustness, Explainable AI (XAI), Cybercrime Investigation