SoK: Timeline based event reconstruction for digital forensics: Terminology, methodology, and current challenges
- URL: http://arxiv.org/abs/2504.18131v1
- Date: Fri, 25 Apr 2025 07:33:35 GMT
- Title: SoK: Timeline based event reconstruction for digital forensics: Terminology, methodology, and current challenges
- Authors: Frank Breitinger, Hudan Studiawan, Chris Hargreaves,
- Abstract summary: Event reconstruction is a technique that examiners can use to attempt to infer past activities by analyzing digital artifacts.<n>Despite its significance, the field suffers from fragmented research, with studies often focusing narrowly on aspects like timeline creation or tampering detection.<n>This paper proposes a comprehensive framework for timeline-based event reconstruction, adapted from traditional forensic science models.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Event reconstruction is a technique that examiners can use to attempt to infer past activities by analyzing digital artifacts. Despite its significance, the field suffers from fragmented research, with studies often focusing narrowly on aspects like timeline creation or tampering detection. This paper addresses the lack of a unified perspective by proposing a comprehensive framework for timeline-based event reconstruction, adapted from traditional forensic science models. We begin by harmonizing existing terminology and presenting a cohesive diagram that clarifies the relationships between key elements of the reconstruction process. Through a comprehensive literature survey, we classify and organize the main challenges, extending the discussion beyond common issues like data volume. Lastly, we highlight recent advancements and propose directions for future research, including specific research gaps. By providing a structured approach, key findings, and a clearer understanding of the underlying challenges, this work aims to strengthen the foundation of digital forensics.
Related papers
- A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond [88.5807076505261]
Large Reasoning Models (LRMs) have demonstrated strong performance gains by scaling up the length of Chain-of-Thought (CoT) reasoning during inference.<n>A growing concern lies in their tendency to produce excessively long reasoning traces.<n>This inefficiency introduces significant challenges for training, inference, and real-world deployment.
arXiv Detail & Related papers (2025-03-27T15:36:30Z) - Detecting Neurocognitive Disorders through Analyses of Topic Evolution and Cross-modal Consistency in Visual-Stimulated Narratives [84.03001845263]
Early detection of neurocognitive disorders (NCDs) is crucial for timely intervention and disease management.<n>Traditional narrative analysis often focuses on local indicators in microstructure, such as word usage and syntax.<n>We propose to investigate specific cognitive and linguistic challenges by analyzing topical shifts, temporal dynamics, and the coherence of narratives over time.
arXiv Detail & Related papers (2025-01-07T12:16:26Z) - Evaluating tamper resistance of digital forensic artifacts during event reconstruction [0.0]
This article proposes a framework to assess the tamper resistance of data sources used in event reconstruction.<n>We discuss factors affecting data resilience, introduce a scoring system for evaluation, and illustrate its application with case studies.
arXiv Detail & Related papers (2024-12-17T11:32:02Z) - Spatio-Temporal Graphical Counterfactuals: An Overview [11.616701619068804]
Counteractual is a critical yet challenging topic for artificial intelligence to learn knowledge from data.
Our aim is to investigate a survey to compare thinking and discuss different counterfactual models, theories and approaches.
arXiv Detail & Related papers (2024-07-02T01:34:13Z) - A Survey on Deep Learning for Theorem Proving [16.28502772608166]
Theorem proving is a fundamental aspect of mathematics, spanning from informal reasoning in natural language to rigorous derivations in formal systems.
Deep learning, especially the emergence of large language models, has sparked a notable surge of research exploring these techniques to enhance the process of theorem proving.
arXiv Detail & Related papers (2024-04-15T17:07:55Z) - Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospects [84.36935309169567]
We present a broad review of recent advances for fine-grained analysis in zero-shot learning (ZSL)
We first provide a taxonomy of existing methods and techniques with a thorough analysis of each category.
Then, we summarize the benchmark, covering publicly available datasets, models, implementations, and some more details as a library.
arXiv Detail & Related papers (2024-01-31T11:51:24Z) - Process Mining for Unstructured Data: Challenges and Research Directions [0.0]
The application of process mining for unstructured data might significantly elevate novel insights into disciplines where unstructured data is a common data format.
To efficiently analyze unstructured data by process mining and to convey confidence into the analysis result, requires bridging multiple challenges.
arXiv Detail & Related papers (2023-11-30T12:09:14Z) - Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges [5.0243930429558885]
Few-Shot Semantic is a novel task in computer vision, which aims at designing models capable of segmenting new semantic classes with only a few examples.
This paper consists of a comprehensive survey of Few-Shot Semantic, tracing its evolution and exploring various model designs.
arXiv Detail & Related papers (2023-04-12T13:07:37Z) - Parsing Objects at a Finer Granularity: A Survey [54.72819146263311]
Fine-grained visual parsing is important in many real-world applications, e.g., agriculture, remote sensing, and space technologies.
Predominant research efforts tackle these fine-grained sub-tasks following different paradigms.
We conduct an in-depth study of the advanced work from a new perspective of learning the part relationship.
arXiv Detail & Related papers (2022-12-28T04:20:10Z) - An Empirical Study: Extensive Deep Temporal Point Process [61.14164208094238]
We first review recent research emphasis and difficulties in modeling asynchronous event sequences with deep temporal point process.
We propose a Granger causality discovery framework for exploiting the relations among multi-types of events.
arXiv Detail & Related papers (2021-10-19T10:15:00Z) - Deep Learning Schema-based Event Extraction: Literature Review and
Current Trends [60.29289298349322]
Event extraction technology based on deep learning has become a research hotspot.
This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
arXiv Detail & Related papers (2021-07-05T16:32:45Z) - A Gentle Introduction to Deep Learning for Graphs [23.809161531445053]
This work is designed as a tutorial introduction to the field of deep learning for graphs.
It introduces a general formulation of graph representation learning based on a local and iterative approach to structured information processing.
It introduces the basic building blocks that can be combined to design novel and effective neural models for graphs.
arXiv Detail & Related papers (2019-12-29T16:43:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.