A Unified Forensic Framework for Data Identification and Collection in Mobile Cloud Social Network Applications

Mobile Cloud Computing (MCC) is the emerging and well accepted concept that significantly removes the constraints of mobile devices in terms of storage and computing capabilities and improves productivity, enhances performance, saves energy, and elevates user experience. The consolidation of cloud computing, wireless communication infrastructure, portable computing devices, location- based services, and mobile web has led to the inauguration of novel computing model. The Mobile social networks and cloud computing technology have gained rapid and intensive attention in recent years because of its numerous available benefits. Despite being an advanced technology to communicate and socialize with friends, the diverse and anonymous nature of mobile cloud social networking applications makes them very vulnerable to crimes and illegal activities. On considering the point of mobile cloud computing benefits, the forensic assistance based mobile cloud computing could offer a solution to the problem of social networking applications. Therefore, this work proposes a Mobile Cloud Forensic Framework (MCFF) to facilitate forensic investigation in social networking applications. The MCFF comprises of two components such as the forensic logging module and the forensic investigation process. The forensic logging module is a readiness component that is installed both the device and on the cloud. The ClouDroid Inspector (CDI) tool uses of the record traced by forensic logging module and conduct the investigation in both the mobile and the cloud. The MCFF identifies and collects the automated synchronized copies of data on both the mobile and cloud environment to prove and establish the use of cloud service via Smartphones.

@article{Faheem2016,
  title     = {A Unified Forensic Framework for Data Identification and Collection in Mobile Cloud Social Network Applications},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {1},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Faheem and Dr Tahar Kechadi and Dr An Le Khac},
  doi       = {10.14569/IJACSA.2016.070103},
  url       = {https://doi.org/10.14569/IJACSA.2016.070103}
}