| Popis: |
In an era marked by technological advancement, the rising reliance on Virtual Private Networks (VPNs) necessitates sophisticated forensic analysis techniques to investigate VPN traffic, especially in mobile environments. This research introduces an innovative approach utilizing Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) for classifying VPN traffic, aiding forensic investigators in precisely identifying applications or websites accessed via VPN connections. By leveraging the combined strengths of CNNs and GNNs, our method provides an effective solution for discerning user activities during VPN sessions. Further extending this framework, we incorporate blockchain technology to meticulously record all mobile VPN transactions, ensuring a tamper-proof and transparent ledger that significantly bolsters the integrity and admissibility of forensic evidence in legal scenarios. A specific use-case demonstrates this methodology in mobile forensics, where our integrated approach not only accurately classifies data traffic but also securely logs transactional details on the blockchain, offering an unprecedented level of detail and reliability in forensic investigations. Extensive real-world VPN dataset experiments validate our approach, highlighting its potential to achieve high accuracy and offering invaluable insights for both technological and legal domains in the context of mobile VPN usage. |
