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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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Book ChapterDOI
24 Sep 2014
TL;DR: This paper proposes a heuristic model for performing digital forensics in the cloud environment with respect to the cloud user as well as the provider and focuses on the methods of finding and analyzing digital evidence in cloud computing environment.
Abstract: Cloud computing is a relatively new model in the computing world after several computing paradigms like personal, ubiquitous, grid, mobile, and utility computing. Cloud computing is synonymous with virtualization which is about creating virtual versions of the hardware platform, the Operating System or the storage devices. Virtualization poses challenges to implementation of security as well as cybercrime investigation in the cloud. Although several researchers have contributed in identifying digital forensic challenges and methods of performing digital forensic analysis in the cloud computing environment, we feel that the requirement of finding the most appropriate methods to evaluate the uncertainty in the digital evidence is a must. This paper emphasizes on the methods of finding and analyzing digital evidence in cloud computing environment with respect to the cloud user as well as the provider. We propose a heuristic model for performing digital forensics in the cloud environment.

13 citations

Dissertation
01 Jan 2015
TL;DR: This study is aimed to fill the gap identified in the literature; there is no investigation process model that can be used on an investigation that involves multi-disciplinary requirements for SMART devices.
Abstract: Worldwide usage of mobile SMART devices has been dramatically increased over the past decade. The popularity of these devices has also grown as a result of the increase in terms of their processing power, large storage capacity and large memory. Mobile SMART devices such as SMART phones, tablet, phaplets and Personal Digital Assistants (PDAs) are now very common and very much part of most businesses’ network. As a result, these devices hold enormous amounts of both personal and private business data. Consequently, they have become the target for criminals. They have been found to be involved in criminal activities particularly cybercrimes. These devices are often seized as part of a criminal investigation, and this has led to the need to acquire the data contained in these devices. The SMART device data has become potential evidence in criminal cases. The vital information held by these mobile SMART devices trigger the need for mobile SMART device forensic capability. The primary aim of digital forensic is to identify the digital information and capture all potential evidence in the device, including call logs, phone book data, text messages, and so on. This process is very important therefore, potential evidence must not be altered in the process so it can be admissible in a court of law. This requires following standardised investigation procedures. However, there is currently no standardised digital forensic investigation process model for SMART devices. Yet, there are a number of digital investigation process models available. However, they were either developed for a specific sub-field such as computer forensics, mobile forensics, and network forensics or, a generic digital forensic investigation model. This study is aimed to fill the gap identified in the literature; there is no investigation process model that can be used on an investigation that involves multi-disciplinary requirements. The question raised here is “What can be done to improve the effectiveness and efficiency of digital forensic investigation for SMART devices?” this question will be answered in chapter seven. This study involves developing of a new digital forensic investigation process model and a framework. To answer the research question and make sure that the new artefact is evaluated and refined to a high standard, the Design

13 citations

Proceedings ArticleDOI
01 Jul 2013
TL;DR: A class of hopping based spread-spectrum techniques for forensic trace back, which fully use the benefits of the spread spectrum approach and preserves a greater degree of secrecy are presented.
Abstract: Network-based crime has been increasing in both extent and severity and network-based forensics encapsulates an essential part of legal surveillance. A key network forensics tool is trace back, which can be used to identify true sources of suspects. Both accuracy and secrecy are essential attributes of a successful forensic trace back. In this paper, we present a class of hopping based spread-spectrum techniques for forensic trace back, which fully use the benefits of the spread spectrum approach and preserves a greater degree of secrecy. Our proposed techniques, including Code Hopping-Direct Sequence Spread Spectrum (CHDSSS), Frequency Hopping-Direct Sequence Spread Spectrum (FH-DSSS), and Time Hopping-Spread Spectrum (TH-DSSS), operate to randomize the effects of marking traffic through both the time and frequency domains. Our simulation study validates these techniques in terms of accuracy and secrecy.

13 citations

Proceedings ArticleDOI
01 Feb 2015
TL;DR: An algorithm that is divided in two parts: computing the repeated frames by processing the image pixels to produce a frame-by-frame motion energy time and computing the tampering attack and its location with the help of the Support Vector Machine helps to predict whether the given video has been tampered or not.
Abstract: The large amount of video content is being transmitted over internet and other channels. With the help of existing multimedia editing tools one can easily change the content of data which lead to lose the authenticity of the information. Thus, it becomes necessary to develop different methods by which the authenticity of the videos can be confirmed. In the past researchers have proposed several methods for authentication of videos. This paper presents an algorithm that is divided in two parts: computing the repeated frames by processing the image pixels to produce a frame-by-frame motion energy time and computing the tampering attack and its location with the help of the Support Vector Machine. This helps to predict whether the given video has been tampered or not.

13 citations

Book ChapterDOI
01 Jan 2020
TL;DR: Social media offers various avenues for the collection and use of its data as evidence within a digital forensics investigation and the advantageous and disadvantageous aspects of this use are discussed in this chapter.
Abstract: Evidence collected from social media presents valuable information that should not be overlooked. Evidence can be captured from social media using multiple methods including searching publicly viewable content, reviewing content metadata, soliciting and investigating interactions with other users, and utilizing legal holds. After the evidence is gathered, it can be utilized in various ways. Social media evidence can be used to create a timeline of events, show intent or conspiracy, and establish connections between persons. Digital forensics investigations can be used to collect such evidence. The evidence accumulated from social media is far-reaching and widely advantageous. However, there are many legal issues that can affect the collection and ultimate legal admissibility of this evidence. Evidence must be collected using careful, correct procedures and in a manner that ensures its integrity. The ethical implications of the collection of social media evidence also plays a role in these digital forensics investigations. These issues can present adverse circumstances for cases. Nonetheless, social media offers various avenues for the collection and use of its data as evidence within a digital forensics investigation. The advantageous and disadvantageous aspects of this use are discussed in this chapter.

13 citations


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