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Development and Design Strategies of Evidence Collection Framework in Cloud Environment

  • Yunus Khan
  • Sunita VarmaEmail author
Conference paper
  • 19 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 100)

Abstract

Nowadays, cloud computing is one of the popular and widely used concepts in information technology paradigm. It is committed to improving the IT business technically and economically. On the other hand, digital forensic is the process of collection, identification, preservation, examination, and analysis of data or information for the proof in the court of law as an evidence. It is very difficult and challenging to apply digital forensic operation in a cloud environment because CSPs are dependent on each other either they provide IaaS, PaaS, or SaaS. So the cloud forensic, one of the applications of digital forensic in a cloud environment, is just a subset of network forensic. It is a cross-field of digital forensic and cloud computing. In this paper, we investigate all the research issues, problems, and implementation ethics of cloud forensic from the initial level. We found that lots of issues and challenges are remaining to address in this domain. Some major research domains are architectures, data collection and analysis, anti-forensic, incident first responders, roles and responsibilities, legal, standards, and some learning issues. In our research work, we mainly focus on the data collection and cloud forensic architectures and also implement a cloud forensic framework in the context of cloud service models. This research work is tested using different private cloud solutions such as eucalyptus, OpenNebula, VMware, vCloud, and Hadoop platform. In our research work, we implement pattern search facility using the proposed approach in open-source software called digital forensic framework. We also implement in near future digital forensic triage using Amazon Elastic MapReduce. In this research, we also implement designed and development of forensic method for the PaaS and SaaS delivery models of cloud computing, also apply machine learning principles to design and develop new digital forensic methods, and improve the efficiency of investigation using machine learning algorithms for feature extraction and priority of evidence classification of evidence in virtual machines.

Keywords

Digital forensic Data collection Evidence segregation Dependency chains IDS Multiple jurisdictions and tenancy IaaS SaaS PaaS SLA Virtual environment VMware 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Shri Govindram Seksaria Institute of Technology and ScienceIndoreIndia

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