Abstract
Advances in Soft Computing have increased the probabilities of implementing mechanisms that are able to predict human behaviour. One of the fields that benefits more from the particular improvements are Digital Forensics. Criminal activity involving smartphones shows interesting behavioural variations that led the authors to create a technique that analyzes smartphone users’ activity and recognizes potentially suspicious patterns according to predefined expert knowledge in actual use case scenarios by the use of fuzzy systems with different configurations.
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Acknowledgments
This work was partially funded by the ATENA H2020 EU Project (H2020-DS-2015-1 Project 700581). We also thank the team of FP7 Project SALUS (Security and interoperability in next generation PPDR communication infrastructures) and the GEPTD officer Nikolaos Bouzis for the fruitful discussions, feedback and insights on in-field investigation practices.
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A SMS Datasets Evaluation Metrics
A SMS Datasets Evaluation Metrics
The appendix contains the analytical metrics for all the datasets tested in Sect. 4 as supplementary resources. Table 3 corresponds to the dataset of the second device (Dev. 2), whereas Table 4 refers to the dataset of the third device (Dev. 3).
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Barmpatsalou, K., Cruz, T., Monteiro, E., Simoes, P. (2018). Fuzzy System-Based Suspicious Pattern Detection in Mobile Forensic Evidence. In: Matoušek, P., Schmiedecker, M. (eds) Digital Forensics and Cyber Crime. ICDF2C 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 216. Springer, Cham. https://doi.org/10.1007/978-3-319-73697-6_8
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