Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Computer Engineering
A Forensic Email Analysis Tool Using Dynamic Visualization, Johannes Stadlinger, Andreas Dewald
A Forensic Email Analysis Tool Using Dynamic Visualization, Johannes Stadlinger, Andreas Dewald
Journal of Digital Forensics, Security and Law
Communication between people counts to the most important information of today’s business. As a result, in case of forensic investigations in big companies, analysis of communication data in general and especially email, as the still most widely used business communication platform with an immense and still growing volume, is a typical task in digital forensics. One of the challenges is to identify the relevant communication partners and structures in the suspects surrounding as quickly as possible in order to react appropriately and identify further targets of evaluation. Due to the amount of emails in typical inboxes, reading through all the …
Making Sense Of Email Addresses On Drives, Neil C. Rowe, Riqui Schwamm, Michael R. Mccarrin, Ralucca Gera
Making Sense Of Email Addresses On Drives, Neil C. Rowe, Riqui Schwamm, Michael R. Mccarrin, Ralucca Gera
Journal of Digital Forensics, Security and Law
Drives found during investigations often have useful information in the form of email addresses which can be acquired by search in the raw drive data independent of the file system. Using this data we can build a picture of the social networks that a drive owner participated in, even perhaps better than investigating their online profiles maintained by social-networking services because drives contain much data that users have not approved for public display. However, many addresses found on drives are not forensically interesting, such as sales and support links. We developed a program to filter these out using a Naïve …
Technology Corner Visualising Forensic Data: Evidence (Part 1), Damian Schofield, Ken Fowle
Technology Corner Visualising Forensic Data: Evidence (Part 1), Damian Schofield, Ken Fowle
Journal of Digital Forensics, Security and Law
Visualisation is becoming increasingly important for understanding information, such as investigative data (for example: computing, medical and crime scene evidence) and analysis (for example: network capability assessment, data file reconstruction and planning scenarios). Investigative data visualisation is used to reconstruct a scene or item and is used to assist the viewer (who may well be a member of the general public with little or no understanding of the subject matter) to understand what is being presented. Analysis visualisations, on the other hand, are usually developed to review data, information and assess competing scenario hypotheses for those who usually have an …
Technology Corner: Visualising Forensic Data: Evidence Guidelines (Part 2), Damian Schofield, Ken Fowle
Technology Corner: Visualising Forensic Data: Evidence Guidelines (Part 2), Damian Schofield, Ken Fowle
Journal of Digital Forensics, Security and Law
Visualisation is becoming increasingly important for understanding information, such as investigative data (for example: computing, medical and crime scene evidence) and analysis (for example, network capability assessment, data file reconstruction and planning scenarios). Investigative data visualisation is used to reconstruct a scene or item and is used to assist the viewer (who may well be a member of the general public with little or no understanding of the subject matter) to understand what is being presented. Analysis visualisations, on the other hand, are usually developed to review data, information and assess competing scenario hypotheses for those who usually have an …
Visualisation Of Honeypot Data Using Graphviz And Afterglow, Craig Valli
Visualisation Of Honeypot Data Using Graphviz And Afterglow, Craig Valli
Journal of Digital Forensics, Security and Law
This research in progress paper explores the use of Graphviz and Afterglow for the analysis of data emanating from a honeypot system. Honeypot systems gather a wide range of data that is often difficult to readily search for patterns and trends using conventional log file analysis techniques. The data from the honeypots has been statically extracted and processed through Afterglow scripts to produce inputs suitable for use by the DOT graph based tools contained within Graphviz. This paper explores some of the benefits and drawbacks of currently using this type of approach.