Understanding issues and challenges of DFR implementation in SDN platform

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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.