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Digital forensics

About: Digital forensics is a research topic. Over the lifetime, 4270 publications have been published within this topic receiving 49676 citations. The topic is also known as: digital forensic science & Digital forensics.


Papers
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Journal ArticleDOI
TL;DR: This paper proposes methods to automate recovery and analysis of Windows NT5 (XP and 2003) event logs for computer forensics, and presents a new, freely available tool that automates the repair of a common type of corruption often observed in data carved NT5 event logs.

21 citations

Journal ArticleDOI
23 Jun 2016
TL;DR: This paper presents the use of Design Science Research (DSR) as a research methodology to develop a Generic Database Forensic Investigation Process Model (DBFIPM), which allows the reconciliation of concepts and terminologies of all common databases forensic investigation processes.
Abstract: Database Forensic investigation is a domain which deals with database contents and their metadata to reveal malicious activities on database systems. Even though it is still new, but due to the overwhelming challenges and issues in the domain, this makes database forensic become a fast growing and much sought after research area. Based on observations made, we found that database forensic suffers from having a common standard which could unify knowledge of the domain. Therefore, through this paper, we present the use of Design Science Research (DSR) as a research methodology to develop a Generic Database Forensic Investigation Process Model (DBFIPM). From the creation of DBFIPM, five common forensic investigation processes have been proposed namely, the i) identification, ii) collection, iii) preservation, iv) analysis and v) presentation process. From the DBFIPM, it allows the reconciliation of concepts and terminologies of all common databases forensic investigation processes. Thus, this will potentially facilitate the sharing of knowledge on database forensic investigation among domain stakeholders.

21 citations

Book ChapterDOI
13 Feb 2005
TL;DR: This paper examines the latest techniques for hiding data in the popular Ext2 and Ext3 file systems and describes techniques for detecting hiddenData in the reserved portions of these file systems.
Abstract: The use of digital forensic tools by law enforcement agencies has made it difficult for malicious individuals to hide potentially incriminating evidence. To combat this situation, the hacker community has developed anti-forensic tools that remove or hide electronic evidence for the specific purpose of undermining forensic investigations. This paper examines the latest techniques for hiding data in the popular Ext2 and Ext3 file systems. It also describes techniques for detecting hidden data in the reserved portions of these file systems.

21 citations

01 Jan 2008
TL;DR: The adoption of existing technologies for digital curation, most especially digital capture, is outlined in the context of personal digital archives and the Digital Manuscripts Project at the British Library.
Abstract: The adoption of existing technologies for digital curation, most especially digital capture, is outlined in the context of personal digital archives and the Digital Manuscripts Project at the British Library. Technologies derived from computer forensics, data conversion and classic computing, and evolutionary computing are considered. The practical imperative of moving information to modern and fresh media as soon as possible is highlighted, as is the need to retain the potential for researchers of the future to experience the original look and feel of personal digital objects. The importance of not relying on any single technology is also emphasised.

21 citations

Journal ArticleDOI
TL;DR: A new convolutional neural network model is proposed to estimate the resampling rate for resampled images regardless of whether the image is upscaled or downscaled and the METEOR layer is demonstrated to be an outstanding method that can assist in enhancing the estimation performance of the CNN.
Abstract: In recent years, with the improvements in machine learning, image forensics has made considerable progress in detecting editing manipulations. This progress also raises more questions in image forensics research, such as can the parameters applied in a manipulation be estimated. Many parameter estimation works have already been performed. However, most of these works are based on mathematical analyses. In this paper, we attempt to solve a particular parameter estimation problem from a different aspect. Specifically, a new convolutional neural network (CNN) model is proposed to estimate the resampling rate for resampled images regardless of whether the image is upscaled or downscaled. This model features an original layer to generate a measurable energy map toward the estimation of resampling rate (METEOR). The METEOR layer is demonstrated to be an outstanding method that can assist in enhancing the estimation performance of the CNN. Furthermore, the METEOR layer can also increase the robustness of the CNN against JPEG compression, which makes it extremely important in realistic application scenarios. Our work has verified that machine learning, particularly CNNs, with proper optimization can also be refined to adapt to parameter estimation in digital forensics with excellent performance and robustness.

21 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20243
2023205
2022552
2021267
2020339
2019343