273 research outputs found

    Identification and recovery of video fragments for forensics file carving

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    In digital forensics, file carving of video files is an important process in the recovery of video evidence needed for many criminal cases. Traditional carving techniques recover video files based on their file structure. However, these techniques fail in cases where the file is split into several fragments, especially if some of the fragments were overwritten. In this paper, we present a method for identification and recovery process of video fragments if the video Codec specifications were overwritten. It consists of two parts which are detector and validators. The detector looks for sequences of bytes that could be video fragments in forensics image. The validator decides to accept or reject that a given fragment is a part of a video file. Based on the proposed method we implement a prototype which is called VidCarve. We have conducted several experiments to evaluate the proposed method with current video carving tools. Experimental results show that the discussed method can identify video fragments with high rates of precision and recall. The overall performance rate can produce forensically sound evidence and play a vital role in the process of recovery of digital evidence in many criminal cases

    Development of a micro-extruder with vibration mode for microencapsulation of human keratinocytes in calcium alginate

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    Microencapsulation is a promising technique to form microtissues. The existing cell microencapsulation technologies that involved extrusion and vibration are designed with complex systems and required the use of high energy. A micro-extruder with an inclusion of simple vibrator that has the commercial value for creating a 3D cell model has been developed in this work. This system encapsulates human keratinocytes (HaCaT) in calcium alginate and the size of the microcapsules is controllable in the range of 500-800 µm by varying the flow rates of the extruded solution and frequency of the vibrator motor ( I 0-63 Hz). At 0.13 ml/min of flow rate and vibration rate of 26.4 Hz, approximately 40 ± IO pieces of the alginate microcapsules in a size 632.14 ± I 0.35 µm were produced. Approximately I 00 µm suspension of cells at different cells densities of 1.55 x I 05 cells/ml and 1.37 x I 07 cells/ml were encapsulated for investigation of microtissues formation. Fourier transform infrared spectroscopy (FTIR) analysis showed the different functional groups and chemistry contents of the calcium alginate with and without the inclusion of HaCaT cells in comparison to the monolayers of HaCaT cells. From Field Emission Scanning Electron Microscope (FESEM) imaging, calcium alginate microcapsules were characterised by spherical shape and homogenous surface morphology. Via the nuclei staining, the distance between cells was found reduced as the incubation period increased. This indicated that the cells merged into microtissues with good cell-cell adhesions. After 15 days of culture, the cells were still viable as indicated by the fluorescence green expression of calcein­acetoxymethyl. Replating experiment indicated that the cells from the microtissues were able to migrate and has the tendency to form monolayer of cells on the culture flask. The system was successfully developed and applied to encapsulate cells to produce 3D microtissues

    Development of a micro-extruder with vibration mode for microencapsulation of human keratinocytes in calcium alginate

    Get PDF
    Microencapsulation is a promising technique to form microtissues. The existing cell microencapsulation technologies that involved extrusion and vibration are designed with complex systems and required the use of high energy. A micro-extruder with an inclusion of simple vibrator that has the commercial value for creating a 3D cell model has been developed in this work. This system encapsulates human keratinocytes (HaCaT) in calcium alginate and the size of the microcapsules is controllable in the range of 500-800 µm by varying the flow rates of the extruded solution and frequency of the vibrator motor ( I 0-63 Hz). At 0.13 ml/min of flow rate and vibration rate of 26.4 Hz, approximately 40 ± IO pieces of the alginate microcapsules in a size 632.14 ± I 0.35 µm were produced. Approximately I 00 µm suspension of cells at different cells densities of 1.55 x I 05 cells/ml and 1.37 x I 07 cells/ml were encapsulated for investigation of microtissues formation. Fourier transform infrared spectroscopy (FTIR) analysis showed the different functional groups and chemistry contents of the calcium alginate with and without the inclusion of HaCaT cells in comparison to the monolayers of HaCaT cells. From Field Emission Scanning Electron Microscope (FESEM) imaging, calcium alginate microcapsules were characterised by spherical shape and homogenous surface morphology. Via the nuclei staining, the distance between cells was found reduced as the incubation period increased. This indicated that the cells merged into microtissues with good cell-cell adhesions. After 15 days of culture, the cells were still viable as indicated by the fluorescence green expression of calcein­acetoxymethyl. Replating experiment indicated that the cells from the microtissues were able to migrate and has the tendency to form monolayer of cells on the culture flask. The system was successfully developed and applied to encapsulate cells to produce 3D microtissues

    Reassembly and clustering bifragmented intertwined jpeg images using genetic algorithm and extreme learning machine

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    File carving tools are essential element of digital forensic investigation for recovering evidence data from computer disk drives. Today, JPEG image files are popular file formats that have less structured contents which make its carving possible in the absence of any file system metadata. However, completely recovering intertwined Bifragmented JPEG images into their original form without missing any parts or data of the image is a challenging due to the intertwined case might occur with non-JPEG images such as PDF, Text, Microsoft Office or random data. In this research, a new carving framework is presented in order to address the fragmentation issues that often occur in JPEG images which is called RX_myKarve. The RX_myKarve is an extended framework from X_myKarve, which consists of the following key components: (i) an Extreme Learning Machine (ELM) neural network for clusters classification using three existing content-based features extraction (Entropy, Byte Frequency Distribution (BFD) and Rate of Change (RoC)) to improve the identification of JPEG images content and support the reassembling process; (ii) a genetic algorithm with Coherence Euclidean Distance (CED) matric and cost function to reconstruct a JPEG image from a set of deformed and fragmented clusters in the scan area. The RX_myKarve is a framework that contains both structure-based carving and content-based carving approaches. The RX_myKarve is implemented as an Automatic JPEG Carver (AJC) tool in order to test and compare its performance with the state-of-the art carvers such as RevIt, myKarve and X_myKarve. It is applied to three datasets namely DFRWS (2006 and 2007) forensic challenges datasets and a new dataset to test and evaluate the AJC tool. These datasets have complex challenges that simulate particular fragmentation cases addressed in this research. The final results show that the AJC with the aid of the RX_myKarve framework outperform the X_myKarve, myKarve and RevIt. The RX_myKarve is able to completely carve 23.8% images more than X_myKarve, 45.4% images more than myKarve and 67% images more than RevIt in which AJC tool using RX_myKarve completely solves the research problem

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

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    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
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