Enhancing Cyber Forensic Investigations Using Deep Learning Methods

Abstract

The exponential growth of cybercrime activities has intensified the demand for advanced digital forensic investigation techniques capable of processing vast amounts of heterogeneous data efficiently. Traditional forensic approaches struggle with the complexity and volume of modern digital evidence, necessitating innovative solutions. This study investigates the application of deep learning methods, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, in enhancing cyber forensic investigations. The research aims to evaluate the effectiveness of deep learning algorithms in automated evidence classification, anomaly detection, and threat analysis within forensic contexts. A comprehensive experimental design was implemented using benchmark datasets including NSL-KDD, Bot-IoT, and CSE-CIC-IDS2018 to assess model performance. The methodology incorporates hybrid CNN-LSTM architectures with attention mechanisms for optimal feature extraction and temporal pattern recognition. Results demonstrate that deep learning approaches achieve superior performance with CNN-LSTM models attaining 99.87% accuracy, 99.89% precision, and 99.85% recall in threat detection tasks. Statistical analysis reveals significant improvements in processing time reduction by 57% compared to traditional methods. The findings indicate substantial enhancements in forensic investigation efficiency through automated analysis capabilities. However, challenges remain regarding model interpretability and legal admissibility of AI-generated evidence. The study concludes that deep learning methods represent a transformative technology for modern cyber forensic investigations, offering unprecedented capabilities in handling large-scale digital evidence while maintaining high accuracy standards for investigative processes.