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Internet of Things for digital forensics Application in Saudi Arabia

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Advances in Internet of Things 2023年第1期13卷 1-11页

作者： Faihan B. Bindrwish Amer Nizar Abu Ali Wed H. Ghabban Alaaldin Alrowwad Najmah Adel Fallatah Omair Ameerbakhsh Ibrahim M. Alfadli Information System Department College of Computer Science and Engineering Taibah University Madina Saudi Arabia Applied College University of Tabuk Tabuk Saudi Arabia Department of Business School The University of Jordan Aqaba Branch Aqaba Jordan

Despite the extensive empirical literature relating to the Internet of Things (IoT), surprisingly few attempts have sought to establish the ways in which digital forensics can be applied to undertake detailed examinations regarding IoT frameworks. The existing digital forensic applications have effectively held back efforts to align the IoT with digital forensic strategies. This is because the forensic applications are ill-suited to the highly complex IoT frameworks and would, therefore, struggle to amass, analyze and test the necessary evidence that would be required by a court. As such, there is a need to develop a suitable forensic framework to facilitate forensic investigations in IoT settings. Nor has considerable progress been made in terms of collecting and saving network and server logs from IoT settings to enable examinations. Consequently, this study sets out to develop and test the FB system which is a lightweight forensic framework capable of improving the scope of investigations in IoT environments. The FB system can organize the management of various IoT devices found in a smart apartment, all of which is controlled by the owner’s smart watch. This will help to perform useful functions, automate the decision-making process, and ensure that the system remains secure. A Java app is utilized to simulate the FB system, learning the user’s requirements and security expectations when installed and employing the MySQL server as a means of logging the communications of the various IoT devices.

关键词： Smart Home Internet of Things digital forensic FB Framework

Multiple Forgery Detection in Video Using Convolution Neural Network

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Computers, Materials & Continua 2022年第10期73卷 1347-1364页

作者： Vinay Kumar Vineet Kansal Manish Gaur Centre for Advanced Studies Dr.A P J Abdul Kalam Technical UniversityLucknowIndia Institute of Engineering and Technology Dr.A P J Abdul Kalam Technical UniversityLucknowIndia

With the growth of digital media data manipulation in today’s era due to the availability of readily handy tampering software,the authenticity of records is at high risk,especially in *** is a dire need to detect such problem and do the necessary *** this work,we propose an approach to detect the interframe video forgery utilizing the deep features obtained from the parallel deep neural network model and thorough analytical *** proposed approach only uses the deep features extracted from the CNN model and then applies the conventional mathematical approach to these features to find the forgery in the *** work calculates the correlation coefficient from the deep features of the adjacent frames rather than calculating directly from the *** divide the procedure of forgery detection into two phases–video forgery detection and video forgery *** video forgery detection,this approach detect input video is original or *** the video is not original,then the video is checked in the next phase,which is video forgery *** the video forgery classification,method review the forged video for insertion forgery,deletion forgery,and also again check for *** proposed work is generalized and it is tested on two different *** experimental results of our proposed model show that our approach can detect the forgery with the accuracy of 91%on VIFFD dataset,90%in TDTV dataset and classify the type of forgery–insertion and deletion with the accuracy of 82%on VIFFD dataset,86%on TDTV *** work can helps in the analysis of original and tempered video in various domain.

关键词： digital forensic forgery detection video authentication video interframe forgery video processing deep learning

Clothing identification via deep learning:forensic applications

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forensic Sciences Research 2018年第3期3卷 219-229页

作者： Marianna Bedeli Zeno Geradts Erwin van Eijk University of Amsterdam AmsterdamThe Netherlands Netherlands forensic Institute(NFI) Den HagueThe Netherlands

Attribute-based identification systems are essential for forensic investigations because they help in identifying *** item such as clothing is a visual attribute because it can usually be used to describe *** method proposed in this article aims to identify people based on the visual information derived from their *** learning is used to train the computer to classify images based on clothing *** first demonstrate clothing classification using a large scale dataset,where the proposed model performs relatively ***,we use clothing classification on a dataset containing popular logos and famous brand *** results show that the model correctly classifies most of the test images with a success rate that is higher than 70%.Finally,we evaluate clothing classification using footage from surveillance *** system performs well on this dataset,labelling 70%of the test images correctly.

关键词： forensic sciences digital forensic clothing classification attribute identification systems deep learning large scale dataset surveillance camera

Evaluating OpenFace:an open-source automatic facial comparison algorithm for forensics

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forensic Sciences Research 2018年第3期3卷 202-209页

作者： Angeliki Fydanaki Zeno Geradts Netherlands forensic Institute University of AmsterdamAmsterdamThe Netherlands

This article studies the application of models of OpenFace(an open-source deep learning algorithm)to forensics by using multiple *** discussion focuses on the ability of the software to identify similarities and differences between faces based on images from *** using OpenFace on the Labeled Faces in the Wild(LFW)-raw dataset,the LFW-deep funnelled dataset,the Surveillance Cameras Face Database(SCface)and ForenFace datasets showed that as the resolution of the input images worsened,the effectiveness of the models *** general,the effect of the quality of the query images on the efficiency of OpenFace was ***,OpenFace in its current form is inadequate for application to forensics,but can be improved to offer promising uses in the field.

关键词： forensic sciences digital forensic face comparison OpenFace deep learning

Enhancing Police Officers’ Cybercrime Investigation Skills Using a Checklist Tool

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Journal of Data Analysis and Information Processing 2023年第2期11卷 121-143页

作者： Abdelkhalek Ibrahim Alastal Ashraf Hassan Shaqfa Geography and GIS Department Arts College Islamic University of Gaza Gaza Palestine

This paper addressed the current state of police officers’ capabilities, skills, and their readiness to deal with the developments of cybercrime. This study discussed definition of cybercrime, cybercrime categories as well as comparison between traditional criminal techniques and cybercrime. As the abilities and skills required for detectives to investigate cybercrime have been discussed. Additionally, literature review and related work, was addressed challenges role of the police in combating cybercrime and facing cybercrime policing. We proposed the main tool in the study which is “Checklist of essential skills for a cybercrime investigator”. Thus, to gain the ability to Identify technical and practical requirements in terms of skills, programs, and equipment to achieve effective and professional results in fight cybercrimes.

关键词： Cybercrimes Cyber Security digital forensic Police Officer Skills Checklist

Modeling digital Evidence Management and Dynamics Using Petri Nets

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Computer Technology and Application 2011年第7期2卷 545-549页

作者： Jasmin Cosic Zoran Cosic Miroslav Baca IT Section of Police Administration Ministry of the Interior Bihac 77000 Bosnia and Herzegovina Statheros Kastel Stari Split Croatia Faculty of Organization and Informatics University of Zagreb Varazdin 42000 Croatia

In all phases of forensic investigation, digital evidence is exposed to external influences and coming into contact with many factors. Legal admissibility of digital evidence is the ability of that evidence being accepted as evidence in a court of law. Life cycle of digital evidence is very complex. In each stage there is more impact that can violate a chain of custody and its integrity. Contact with different variables occurs through a life cycle of digital evidence and can disrupt its integrity. In order for the evidence to be accepted by the court as valid, chain of custody for digital evidence must be kept, or it must be known who exactly came into contact with evidence in each stage of the investigation. This paper presents a dynamics and life cycle of digital evidence. The Petri nets will be proposed and used for modeling and simulation of this process.

关键词： digital evidence digital forensic chain of custody digital evidence integrity digital evidence manipulating Petri nets