Understanding issues and challenges of DFR implementation in SDN platform
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Date
Authors
Munkhondya, Howard
Ikuesan, Richard Adeyemi
Singh, Avinash
Venter, H.S. (Hein)
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Software-Defined Networking (SDN) is an evolutionary networking paradigm that offers simplified and agile network configuration and management capabilities. However, embracing this new and futuristic paradigm requires the understanding of Digital Forensics (DF) limitations that it presents. Studies show that the dynamism of SDN architecture impedes the preservation of Potential Digital Evidence (PDE) during a Digital Forensic Readiness (DFR) process. Therefore, the identification and acquisition of viable PDE in SDN platforms largely depends on the thorough understanding of the issues and challenges affecting the application of DFR in SDN platforms. For this reason, this study leverages a case study research methodology to empirically underline the forensic limitations and provide level of specificity with which these limitations affect the DFR process. The results of the case study combined with existing literature are used to expose the issues and challenges in a typical SDN testbed. The knowledge acquired from the state-of-the-art with respect to conducting DFR in an SDN platform addresses the knowledge gap of understanding these limitations.
Description
Paper presented at CENTERIS – International Conference on ENTERprise Information Systems / ProjMAN –
International Conference on Project MANagement / HCist – International Conference on Health
and Social Care Information Systems and Technologies 2022.
Keywords
Software-defined networking (SDN), Digital forensics, Potential digital evidence (PDE), Digital forensic readiness (DFR), SDN forensics, SDN forensic readiness
Sustainable Development Goals
Citation
Munkhondya, H., Ikuesan, R.A., Singh, A. & Venter, H. 2023, 'Understanding issues and challenges of DFR implementation in SDN platform', Procedia Computer Science, vol. 219, pp. 286-293, doi : 10.1016/j.procs.2023.01.292.