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

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

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

Digital Forensics Overview

Digital Evaluation and Exploitation (DEEP): Research in "trusted" systems and exploitation

Providing cryptographic security and evidentiary chain-of-custody with the advanced forensic format, library, and tools

This paper presents improvements in the Advanced Forensics Format Library version 3 that provide for digital signatures and other cryptographic protections for digital evidence, allowing an investigator to establish a reliable chain-of-custody for electronic evidence from the crime scene to the court room. No other system for handling and storing electronic evidence currently provides such capabilities. This paper discusses implementation details, user level commands, and the AFFLIB programmer's API.Approved for public release; distribution is unlimited

Forensic analysis of deduplicated file systems

Deduplication splits files into fragments, which are stored in a chunk repository. Deduplication stores chunks that are common to multiple files only once. From a forensics point of view, a deduplicated device is very difficult to recover and it requires a specific knowledge of how this technology operates. Deduplication starts from a whole file, and transforms it in an organized set of fragments. In the recent past, it was reserved to datacenters, and used to reduce space for backups inside virtual tape library (VTL) devices. Now this technology is available in open source packages like OpenDedup, or directly as an operating system feature, as in Microsoft Windows Server or in ZFS. Recently Microsoft included this feature in Windows 10 Technical Preview. Digital investigation tools need to be improved to detect, analyze and recover the content of deduplicated file systems. Deduplication adds a layer to data access that needs to be investigated, in order to act correctly during seizure and further analysis. This research analyzes deduplication technology in the perspective of a digital forensic investigation

Forensic Data Properties of Digital Signature BDOC and ASiC-E Files on Classic Disk Drives

Käesolevas magistritöös vaadeldakse BDOC ja ASiC-E digitaalselt allkirjastatud dokumendikonteinerite sisu ning kirjeldatakse nende huvipakkuvaid omadusi. Teatava hulga näidiskonteinerite vaatlemise järel pakub autor välja faili päise ja faili jaluse kombinatsiooni (signatuuri), mis oluliselt parandab nimetatud failide kustutatud olekust sihitud taastamist külgnevatest klastritest NTFS vormindatud tihendamata kettal, võttes arvesse klassikalise kõvaketta geomeetriat. Ühtlasi kirjeldab autor kohtuekspertiisi koha pealt tähendust omavaid andmeid ZIP kohaliku faili päises ja keskkataloogi kirjes, XML signatuuris ja ASN.1 kodeeritud kihtides ning nende kättesaamise algoritmi. Nendele järeldustele tuginedes loob autor Phytoni skripte ja viib läbi mitmeid teste failide taastamiseks faili signatuuri järgi ning huvipakkuvate andmete väljavõtmiseks. Teste viiakse läbi teatava valiku failide üle ja tulemusi võrreldakse mitme kohtuekspertiisis laialt kasutatava peavoolu töökeskkonnaga, samuti mõningate andmetaaste tööriistadega. Lõpuks testitakse magistritöö käigus pakutud digitaalselt allkirjastatud dokumentide taastamiseks mõeldud signatuuri ja andmete väljavõtmise algoritmi suurel hulgal avalikust dokumendiregistrist pärit kehtivate dokumentidega, mis saadi kätte spetsiaalselt selleks kirjutatud veebirobotiga. Nimetatud teste viiakse läbi dokumentide üle, mille hulgas on nii digitaalselt allkirjastatud dokumente kui ka teisi, nendega struktuurilt sarnaseid dokumente.This thesis reviews the contents and observes certain properties of digitally signed documents of BDOC and ASiC-E container formats. After reviewing a set of sample containers, the author comes up with a header and footer combination (signature) significantly improving pinpointed carving-based recovery of those files from a deleted state on NTFS formatted uncompressed volumes in contiguous clusters, taking into account the geometry of classic disk drives. The author also describes forensically meaningful attributive data found in ZIP Headers and Central Directory, XML signatures as well as embedded ASN.1 encoded data of the sample files and suggests an algorithm for the extraction of such data. Based on these findings, the author creates scripts in Python and executes a series of tests for file carving and extraction of attributive data. These tests are run over the samples placed into unallocated clusters and the results are compared to several mainstream commercial forensic examination suites as well as some popular data recovery tools. Finally, the author web-scrapes a large number of real-life documents from a government agency’s public document registry. The carving signature and the data-extractive algorithm are thereafter applied on a larger scale and in an environment competitively supplemented with structurally similar containers

Identification of fragmented JPEG files in the absence of file systems

Identifying fragmented and deleted files from scattered digital storage become crucial needs in computer forensic. Storage media experience regular space fragmentation which gives direct consequence to the files system series. This paper specifies a case where the jpeg files are heavily fragmented with absent file header which contains maximum information for the stored data can be easily retrieved. The problem is formulated using statistical byte frequency analysis for identifying the group of jpeg file fragments. Several related works have addressed the issue of classifying variety types of file format with high occurrence of being fragmented such as avi, doc, wav file and etc. These files have been tagged as among the larger file format. We provide techniques for identifying the pattern of file fragments distribution and describe roles of selected clustering attributes. Finally, we provide experimental results presenting that the jpeg fragments distribution can be retrieved with quite small gap differences between the groups

Paper Session V: Steganography and Terrorist Communications - Current Information and Trends - Tools, Analysis and Future Directions in Steganalysis in Context with Terrorists and Other Criminals

In ancient times, users communicated using steganography, “…derived from the Greek words steganos, meaning ‘covered’, and graphein, meaning ‘to write.’” (Singh, 1999, p.5) Steganography facilitates secret, undetected communication. In modern times, in the context of the Global War on Terror, national intelligence and law enforcement agencies need tools to detect hidden information (steganography) in various types of media, most specifically to uncover the placement of hidden information in images. This paper will look at steganography in general terms, presenting the theory of some common steganographic techniques and touching on some theoretical work in steganography. Then a discussion of how to utilize detection tools will shed light on the question of how to make our nation more secure in light of this technology being used by nefarious individuals and organizations. Keywords: Steganography, information hiding, computer forensics, terrorism, steganalysis, cryptograph

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

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