Congratulations to Maziar Foutohi, Shams Zawoad, Ragib Hasan, Abhishek Anand, and Anthony Skjellum for having their work accepted in the 8th IEEE International Conference on Cloud Computing.
1. Shams Zawoad, Ragib Hasan, Anthony Skjellum, “OCF: An Open Cloud Forensics Model for Reliable Digital Forensics”, the 8th IEEE International Conference on Cloud Computing, New York, USA, June 2015
Abstract: The rise of cloud computing has changed the way computing services and resources are used. However, existing digital forensics science cannot cope with the black-box nature of clouds nor with multi-tenant cloud models. Because of the fundamental characteristics of clouds, many assumptions of digital forensics are invalidated in clouds. In the digital forensics process involving clouds, the role of cloud service providers (CSP) is utterly important, a role which needs to be considered in the science of cloud forensics. In this paper, we define cloud forensics considering the role of the CSP and propose the Open Cloud Forensics (OCF) model. Based on this OCF model, we propose a cloud computing architecture and validate our proposed model using a case study, which is inspired from an actual civil lawsuit.
2. Maziar Foutohi, Abhishek Anand, Ragib Hasan, “PLAG: Practical Landmark Allocation for Cloud Geolocation”, the 8th IEEE International Conference on Cloud Computing, New York, USA, June 2015.
Abstract: Knowing the physical location of files in a cloud system is of a great importance for any user, as is it can affect the whole service drastically. However, pinpointing the exact coordinates for the location of a server is very challenging. Providers prefer not to share the location of their data centers with public for security reasons, and this fact also adds to the complexity of this concept. Researchers have recently developed delay based schemes for cloud data geolocation, some of which use proprietary landmarks for location verification. Unfortunately, such landmark-based schemes are often impractical due to high cost and latency. In this paper, we have developed a practical scheme for landmark allocation in cloud data geolocation. We augment existing approaches with a new landmark allocation modification to get the same or often better accuracy, while decreasing the cost considerably. Our approach improves the existing state of the art by introducing the concept of publicly distributed landmarks for all delay based geolocation techniques.
