Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash 90 tok/s
Gemini 2.5 Pro 41 tok/s Pro
GPT-5 Medium 20 tok/s
GPT-5 High 23 tok/s Pro
GPT-4o 93 tok/s
GPT OSS 120B 441 tok/s Pro
Kimi K2 212 tok/s Pro
2000 character limit reached

A complete formalized knowledge representation model for advanced digital forensics timeline analysis (1903.01396v1)

Published 21 Feb 2019 in cs.CY, cs.CR, and cs.LG

Abstract: Having a clear view of events that occurred over time is a difficult objective to achieve in digital investigations (DI). Event reconstruction, which allows investigators to understand the timeline of a crime, is one of the most important step of a DI process. This complex task requires exploration of a large amount of events due to the pervasiveness of new technologies nowadays. Any evidence produced at the end of the investigative process must also meet the requirements of the courts, such as reproducibility, verifiability, validation, etc. For this purpose, we propose a new methodology, supported by theoretical concepts, that can assist investigators through the whole process including the construction and the interpretation of the events describing the case. The proposed approach is based on a model which integrates knowledge of experts from the fields of digital forensics and software development to allow a semantically rich representation of events related to the incident. The main purpose of this model is to allow the analysis of these events in an automatic and efficient way. This paper describes the approach and then focuses on the main conceptual and formal aspects: a formal incident modelization and operators for timeline reconstruction and analysis.

Citations (59)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Ai Generate Text Spark Streamline Icon: https://streamlinehq.com

Paper Prompts

Sign up for free to create and run prompts on this paper using GPT-5.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.