Evidence Preservation in Digital Forensics: An Approach Using Blockchain and LSTM-Based Steganography

Abstract

As digital crime continues to rise, the preservation of digital evidence has become a critical phase in digital forensic investigations. This phase focuses on securing and maintaining the integrity of evidence for legal proceedings. Existing solutions for evidence preservation, such as centralized storage systems and cloud frameworks, present challenges related to security and collaboration. In this paper, we propose a novel framework that addresses these challenges in the preservation phase of forensics. Our framework employs a combination of advanced technologies, including the following: (1) Segmenting evidence into smaller components for improved security and manageability, (2) Utilizing steganography for covert evidence preservation, and (3) Implementing blockchain to ensure the integrity and immutability of evidence. Additionally, we incorporate Long Short-Term Memory (LSTM) networks to enhance steganography in the evidence preservation process. This approach aims to provide a secure, scalable, and reliable solution for preserving digital evidence, contributing to the effectiveness of digital forensic investigations. An experiment using linguistic steganography showed that the LSTM autoencoder effectively generates coherent text from bit streams, with low perplexity and high accuracy. Our solution outperforms existing methods across multiple datasets, providing a secure and scalable approach for digital evidence preservation.