Frequent Temporal Inter-object Pattern Mining in Time Series

Vu, Nguyen Thanh; Chau, Vo Thi Ngoc

doi:10.1007/978-3-319-02741-8_15

Cited by 2 publications

(1 citation statement)

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“…Currently, there are some methods that can be used to describe time-series feature, such as piecewise linear representation, segmentation aggregation approximation, symbolic representation, representation of the domain transform, singular value decomposition and model-based approach. Mining time series for useful hidden evidence information is very significant in digital forensics to help investigators get fascinating insights into important temporal relationships of scenario/phenomena along the time (Vu, & Chau, 2014). The authors also proposed a notion of frequent temporal interobject pattern and accordingly provided two frequent temporal pattern mining algorithms on a set of different time series.…”

Section: Sequence Analysismentioning

confidence: 99%

Survey of Digital Forensics Technologies and Tools for Android based Intelligent Devices

Wan

Liu

et al. 2015

International Journal of Digital Crime and Forensics

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During the rapid development of mobile wireless technologies and applications, the Android operating system, due to its open-source characteristics, has become the most popular development platform in the smartphone market. Meanwhile, as Android-based intelligent mobiles devices experience a rapid increase in numbers, high-tech crimes involving such devices have become more versatile, affecting an ever increasing amount of data, thus making digital evidence an indispensable part of the evidence that needs to be seriously dealt with during crime investigations. Consequently, understanding the internal structure of Android and the various data operations in the file systems becomes necessary in Android-based digital forensics. In this paper, we survey the state-of-the-art of technologies in Android-based digital forensics and some popular tools in the aspects of data recovery and acquisition, file system analysis and data analysis. We also discuss some technical challenges and point out future research directions in Android-based digital forensics.

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Section: Sequence Analysismentioning

confidence: 99%