Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection

Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization. Nonetheless, motivated by promising results in computer vision, the focus of the research community is now shifting on deep learning. In this paper we show that a class of residual-based descriptors can be actually regarded as a simple constrained convolutional neural network (CNN). Then, by relaxing the constraints, and fine-tuning the net on a relatively small training set, we obtain a significant performance improvement with respect to the conventional detector

Methodology for Investigating Individuals Online Social Networking Persona

When investigators from either the private or public sector review digital data surrounding a case for evidentiary value, they typically conduct a systematic categorization process to identify the relevant digital devices. Armed with the proper methodology to accomplish this task, investigators can quickly recognize the appropriate digital devices for forensic processing and review. This paper purposes a methodology for investigating an individual’s online social networking persona. Keywords: Social Networking, Web 2.0, Internet Investigations, Online Social Networking Communit

Multinational perspectives on information technology from academia and industry

As the term \u27information technology\u27 has many meanings for various stakeholders and continues to evolve, this work presents a comprehensive approach for developing curriculum guidelines for rigorous, high quality, bachelor\u27s degree programs in information technology (IT) to prepare successful graduates for a future global technological society. The aim is to address three research questions in the context of IT concerning (1) the educational frameworks relevant for academics and students of IT, (2) the pathways into IT programs, and (3) graduates\u27 preparation for meeting future technologies. The analysis of current trends comes from survey data of IT faculty members and professional IT industry leaders. With these analyses, the IT Model Curricula of CC2005, IT2008, IT2017, extensive literature review, and the multinational insights of the authors into the status of IT, this paper presents a comprehensive overview and discussion of future directions of global IT education toward 2025

Public Understanding of Cyber Security and Digital Forensics within the UK

© 2019 The Author(s). This an open access work distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Little narrative exists within the literature which focuses on the understanding of cyber security and digital forensics to a much wider audience: the public. This paper’s aim is to capture and examine the perceptions of the public by adding insight into what is understood by the terms and disciplines of ‘digital forensics’ and ‘cyber security’. While cyber security and digital forensics can be recognised by their interdisciplinary nature, the two disciplines are distinct in their approach to criminality. At its simplest, cyber security is concerned with the prevention of an incident and implementation of robust systems, while digital forensics focuses on the response to crime and recovering digital evidence. Public perceptions of these areas are important, as security of systems and digital technologies have been heightened in recent years due to high profile cases where notable and large corporations have seen breaches of sensitive information. This study draws on responses from the public using an online survey taken by 102 participants that asked their views on cyber security and digital forensics. This paper demonstrates that there is an awareness among respondents of both disciplines where participants have associated cyber security predominately with the protection of data and systems and digital forensics as the examination and inspection of digital devices. Additionally, responses have also shown there is a need for further awareness in these fields.Peer reviewe

A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response

In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. However, the use of ML and AI in digital forensics is still in its nascent stages. As a result, this paper gives a thorough and in-depth analysis that goes beyond a simple survey and review. The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This research explores cutting-edge research initiatives that cross domains such as data collection and recovery, the intricate reconstruction of cybercrime timelines, robust big data analysis, pattern recognition, safeguarding the chain of custody, and orchestrating responsive strategies to hacking incidents. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. While the promise of AI in digital forensics is evident, the challenges arising from increasing database sizes and evolving criminal tactics necessitate ongoing collaborative research and refinement within the digital forensics profession. This study examines the contributions, limitations, and gaps in the existing research, shedding light on the potential and limitations of AI and ML techniques. By exploring these different research areas, we highlight the critical need for strategic planning, continual research, and development to unlock AI's full potential in digital forensics and incident response. Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats

Science Maps of Global and Indian Wildlife Forensics: A Comparative Analysis

Science map is a useful tool to understand the structure of a discipline, research networks and collaborations. Wildlife forensics is an emerging field of Forensic Sciences, where science is applied to legal cases involving wildlife. This study is aimed at creating science maps of Wildlife Forensics, both at global level and regional (i.e. India) level using PubMed database. A total of 303 records pertaining to global and 29 records pertaining to India published between 2001 and 2015 are obtained from the PubMed. These bibliometric data are analysed and maps are constructed using MS-Excel spreadsheets, VOSviewer and Pajek software. The study shows the global Wildlife Forensics literature growth showed exponential trend while the contemporary Indian literature showed linear growth trend. Globally A.M. Linacre and N. Mukaida share the first rank while among the Indian authors S.P. Goyal receives the first place. The degree of collaboration is more than 0.9. The journal Forensic Science International is the top ranking journal both internationally and nationally. The research trends in Wildlife Forensics are also found from the study