A Forensic Enabled Data Provenance Model for Public Cloud

Cloud computing is a newly emerging technology where storage, computation and services are extensively shared among a large number of users through virtualization and distributed computing. This technology makes the process of detecting the physical location or ownership of a particular piece of data even more complicated. As a result, improvements in data provenance techniques became necessary. Provenance refers to the record describing the origin and other historical information about a piece of data. An advanced data provenance system will give forensic investigators a transparent idea about the data\u27s lineage, and help to resolve disputes over controversial pieces of data by providing digital evidence. In this paper, the challenges of cloud architecture are identified, how this affects the existing forensic analysis and provenance techniques is discussed, and a model for efficient provenance collection and forensic analysis is proposed

Assessing the evidential value of artefacts recovered from the cloud

Cloud computing offers users low-cost access to computing resources that are scalable and flexible. However, it is not without its challenges, especially in relation to security. Cloud resources can be leveraged for criminal activities and the architecture of the ecosystem makes digital investigation difficult in terms of evidence identification, acquisition and examination. However, these same resources can be leveraged for the purposes of digital forensics, providing facilities for evidence acquisition, analysis and storage. Alternatively, existing forensic capabilities can be used in the Cloud as a step towards achieving forensic readiness. Tools can be added to the Cloud which can recover artefacts of evidential value. This research investigates whether artefacts that have been recovered from the Xen Cloud Platform (XCP) using existing tools have evidential value. To determine this, it is broken into three distinct areas: adding existing tools to a Cloud ecosystem, recovering artefacts from that system using those tools and then determining the evidential value of the recovered artefacts. From these experiments, three key steps for adding existing tools to the Cloud were determined: the identification of the specific Cloud technology being used, identification of existing tools and the building of a testbed. Stemming from this, three key components of artefact recovery are identified: the user, the audit log and the Virtual Machine (VM), along with two methodologies for artefact recovery in XCP. In terms of evidential value, this research proposes a set of criteria for the evaluation of digital evidence, stating that it should be authentic, accurate, reliable and complete. In conclusion, this research demonstrates the use of these criteria in the context of digital investigations in the Cloud and how each is met. This research shows that it is possible to recover artefacts of evidential value from XCP

Challenges of data provenance for cloud forensic investigations

Cloud computing has gained popularity due to its efficiency, robustness and cost effectiveness. Carrying out digital forensic investigations in the cloud is currently a relevant and open issue. The root of this issue is the fact that servers cannot be physically accessed, coupled with the dynamic and distributed nature of cloud computing with regards to data processing and storage. This renders traditional methods of evidence collection impractical. The use of provenance data in cloud forensics is critical as it provides forensic investigators with data history in terms of people, entities and activities involved in producing related data objects. Therefore, cloud forensics requires effective provenance collection mechanisms. This paper provides an overview of current provenance challenges in cloud computing and identifies limitations of current provenance collection mechanisms. Recommendations for additional research in digital provenance for cloud forensics are also presented

Challenges of Data Provenance for Cloud Forensic Investigations

Cloud computing has gained popularity due to its efficiency, robustness and cost effectiveness. Carrying out digital forensic investigations in the cloud is currently a relevant and open issue. The root of this issue is the fact that servers cannot be physically accessed, coupled with the dynamic and distributed nature of cloud computing with regards to data processing and storage. This renders traditional methods of evidence collection impractical. The use of provenance data in cloud forensics is critical as it provides forensic investigators with data history in terms of people, entities and activities involved in producing related data objects. Therefore, cloud forensics requires effective provenance collection mechanisms. This paper provides an overview of current provenance challenges in cloud computing and identifies limitations of current provenance collection mechanisms. Recommendations for additional research in digital provenance for cloud forensics are also presented