Pieterse, HeloiseOliver, DMVan Heerden, Renier P2019-12-122019-12-122019-08Pieterse, H., Oliver, D.M. & Van H.R. 2019. Classifying the Authenticity of Evaluated Smartphone Data. In: IFIP International Conference on Digital Forensics: DigitalForensics, Orlando, FL, USA, 28-30 January 20191868-4238https://link.springer.com/chapter/10.1007/978-3-030-28752-8_3https://link.springer.com/conference/digitalforensicshttp://hdl.handle.net/10204/11258Copyright: 2019. This is a pre-print version. The definitive version of the work is published on the proceedings of IFIP International Conference on Digital Forensics: DigitalForensics 2019, 28-30 January, Orlando, FL, USAAdvances in smartphone technology coupled with the widespread use of such devices to accomplish daily tasks create valuable sources of smartphone data. Such data becomes increasingly important when smartphones are linked to civil or criminal investigations. As with all forms of digital data, smartphone data is susceptible to change due to intentional or accidental alterations by end-users or installed applications. It is, therefore, essential to establish the authenticity of smartphone data, before submitting the data as potential evidence. Previously conducted research formulated the smartphone data evaluation model, which provides a methodological approach for evaluating the authenticity of smartphone data. However, the smartphone data evaluation model only stipulates how to evaluate smartphone data without providing a formal outcome regarding the authenticity of the data. This paper introduces a new classi cation model that presents the grade of authenticity of evaluated smartphone data, as well as the completeness of the evaluation. The outcome of a practical experiment con rms the e ective use of the classi cation model to classify the authenticity of smartphone data.enDigital forensicsSmartphone forensicsSmartphonesSmartphone dataAuthenticityConference PresentationPieterse, H., Oliver, D., & Van Heerden, R. P. (2019). Classifying the Authenticity of Evaluated Smartphone Data. http://hdl.handle.net/10204/11258Pieterse, Heloise, DM Oliver, and Renier P Van Heerden. "Classifying the Authenticity of Evaluated Smartphone Data." (2019): http://hdl.handle.net/10204/11258Pieterse H, Oliver D, Van Heerden RP, Classifying the Authenticity of Evaluated Smartphone Data; 2019. http://hdl.handle.net/10204/11258 .TY - Conference Presentation AU - Pieterse, Heloise AU - Oliver, DM AU - Van Heerden, Renier P AB - Advances in smartphone technology coupled with the widespread use of such devices to accomplish daily tasks create valuable sources of smartphone data. Such data becomes increasingly important when smartphones are linked to civil or criminal investigations. As with all forms of digital data, smartphone data is susceptible to change due to intentional or accidental alterations by end-users or installed applications. It is, therefore, essential to establish the authenticity of smartphone data, before submitting the data as potential evidence. Previously conducted research formulated the smartphone data evaluation model, which provides a methodological approach for evaluating the authenticity of smartphone data. However, the smartphone data evaluation model only stipulates how to evaluate smartphone data without providing a formal outcome regarding the authenticity of the data. This paper introduces a new classi cation model that presents the grade of authenticity of evaluated smartphone data, as well as the completeness of the evaluation. The outcome of a practical experiment con rms the e ective use of the classi cation model to classify the authenticity of smartphone data. DA - 2019-08 DB - ResearchSpace DP - CSIR KW - Digital forensics KW - Smartphone forensics KW - Smartphones KW - Smartphone data KW - Authenticity LK - https://researchspace.csir.co.za PY - 2019 SM - 1868-4238 SM - https://link.springer.com/chapter/10.1007/978-3-030-28752-8_3 T1 - Classifying the Authenticity of Evaluated Smartphone Data TI - Classifying the Authenticity of Evaluated Smartphone Data UR - http://hdl.handle.net/10204/11258 ER -