Mobile Forensic Data Analysis: Suspicious Pattern Detection in Mobile Evidence



Culprits’ identification by the means of suspicious pattern detection techniques from mobile device data is one of the most important aims of Mobile Forensic Data Analysis (MFDA). When criminal activities are related to entirely automated procedures such as malware propagation, predicting the corresponding behaviour is a rather achievable task. However, when human behaviour is involved, such as in cases of traditional crimes, prediction and detection become more compelling. The current paper introduces a combined criminal profiling and suspicious pattern detection methodology for two criminal activities with moderate to heavy involvement of mobile devices; cyberbullying and low level drug dealing. Neural and Neurofuzzy techniques are applied on a hybrid original and simulated dataset. The respective performance results are measured and presented, the optimal technique is selected and the scenarios are re-run on an actual dataset for additional testing and verification.


Forensics, Artificial neural networks, Data analysis, Mobile handsets ,Performance evaluation, Fuzzy logic, Tools;Mobile forensics, evidence data analysis, criminal profiling, behavioral evidence analysis, Neural networks, ANFIS

Related Project

H2020 ATENA (Advanced Tools to assEss and mitigate the criticality of ICT compoNents and their dependencies over Critical InfrAstructures)


IEEE Access (Open Access), Vol. 6, pp. 59705-59727, October 2018


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