A Theoretical framework for Harnessing Machine Learning for Digital Forensics in Online Social Networks

Online social networks have become a popular platform for communication and information exchange, but also a target for various cybercriminal activities. Digital forensics is essential for investigating and combating these crimes, but it faces challenges in handling the vast amount of data generated on social network platforms. This paper proposes a machine learning-based digital forensic framework for online social networks, consisting of five stages: data identification, preparation, acquisition, examination, and reporting. The framework employs machine learning algorithms such as Artificial Neural Networks, Decision Trees, and Support Vector Machines for data analysis and evidence extraction in the examination stage. The framework aims to automate data processing and analysis, enabling investigators to focus on understanding crime dynamics and reporting. The paper presents the theoretical analysis of the framework, addressing challenges in digital forensics for online social networks. It also discusses theoretical limitations and future research directions to enhance the framework’s capabilities. A proof-of-concept implementation using a real-world dataset is planned to validate its practicality in solving actual digital forensic investigations. The paper contributes to the advancement of digital forensics for a safer online environment.