Detecting Criminal Relationships through SOM Visual Analytics

Wang, Wen Bo; Huang, Mao Lin; Zhang, Jinson; Lai, Wei

doi:10.1109/iv.2015.62

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…In the context of digital forensics, visualization can be applied to aid forensic analysis and interpretation as well as providing rapid and cost-effective investigations. For example, a graph-based feature analysis model is proposed by Wang et al [15] to reduce and classify original criminal record where visualization techniques allow investigators to visually analyze the associations between features and criminals for better understanding on the activities or possibilities. Heuser et al [16] presented DroidAuditor that applied graph-based presentation to visualize behavior graph in analyzing application-layer privilege escalation attacks in Android platform.…”

Section: A Snapshot Of Related Workmentioning

confidence: 99%

A Forensic Analysis Visualization Tool for Mobile Instant Messaging Apps

Ong¹,

Rahman²

2020

ijoict

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In this study, we demonstrate the role of visualization to facilitate forensic analysis goal in interpreting metadata of evidence of interest to answer who, what, why, when, where, and how an incident occurred. Two mobile Instant Messaging (IM) applications (i.e. WhatsApp and Line) were deployed as a case study. Subsequently, a tool – W*W Visualizer – was designed and developed with the aims to analyze and visualize the connection of evidence metadata, text frequency and word count, and display report of analysis activities. The tool is developed by adopting Object-Oriented Software Development Model with Visual Studio platform and C# language were used to develop the system. Our findings show that W*W Visualizer could transform the data of the chat database into a visual form, for example graph, chart and word cloud. The tool also allows the user to perform search feature such as searching based on keyword and timestamp from the IM chat history. It is expected that outcomes from this study would significantly influence digital forensics practitioners in analyzing and interpreting evidence data, and judicial authorities in understanding the presentation of evidence.

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Section: A Snapshot Of Related Workmentioning

confidence: 99%

A Forensic Analysis Visualization Tool for Mobile Instant Messaging Apps

Ong¹,

Rahman²

2020

ijoict

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A suspect-oriented intelligent and automated computer forensic analysis

Fahdi

Clarke

et al. 2016

Digital Investigation

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Computer forensics faces a range of challenges due to the widespread use of computing technologies. Examples include the increasing volume of data and devices that need to be analysed in any single case, differing platforms, use of encryption and new technology paradigms (such as cloud computing and the Internet of Things). Automation within forensic tools exists, but only to perform very simple tasks, such as data carving and file signature analysis. Investigators are responsible for undertaking the cognitively challenging and time-consuming process of identifying relevant artefacts. Due to the volume of cyber-dependent (e.g., malware and hacking) and cyber-enabled (e.g., fraud and online harassment) crimes, this results in a large backlog of cases. With the aim of speeding up the analysis process, this paper investigates the role that unsupervised pattern recognition can have in identifying notable artefacts. A study utilising the Self-Organising Map (SOM) to automatically cluster notable artefacts was devised using a series of four cases. Several SOMs were created – a File List SOM containing the metadata of files based upon the file system, and a series of application level SOMs based upon metadata extracted from files themselves (e.g., EXIF data extracted from JPEGs and email metadata extracted from email files). A total of 275 sets of experiments were conducted to determine the viability of clustering across a range of network configurations. The results reveal that more than 93.5% of notable artefacts were grouped within the rank-five clusters in all four cases. The best performance was achieved by using a 10 × 10 SOM where all notables were clustered in a single cell with only 1.6% of the non-notable artefacts (noise) being present, highlighting that SOM-based analysis does have the potential to cluster notable versus noise files to a degree that would significantly reduce the investigation time. Whilst clustering has proven to be successful, operationalizing it is still a challenge (for example, how to identify the cluster containing the largest proportion of notables within the case). The paper continues to propose a process that capitalises upon SOM and other parameters such as the timeline to identify notable artefacts whilst minimising noise files. Overall, based solely upon unsupervised learning, the approach is able to achieve a recall rate of up to 93%. © 2016 Elsevier Lt

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Online Review Analysis by Visual Feature Selection

Koka

Fang

2017

2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing,

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Detecting Criminal Relationships through SOM Visual Analytics

Cited by 5 publications

References 35 publications

A Forensic Analysis Visualization Tool for Mobile Instant Messaging Apps

A Forensic Analysis Visualization Tool for Mobile Instant Messaging Apps

A suspect-oriented intelligent and automated computer forensic analysis

Online Review Analysis by Visual Feature Selection

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