Mobiltelefoner har blivit grundläggande för extrahering av digitala artefakter i fo-rensiska utredningar. Androids Linuxbaserade operativsystem medför större möj-ligheter för anti-forensiska metoder, detta gör att kunskap om anti-forensik äressentiell för dagens IT-forensiska utredare. I denna studie belyses effekten avanti-forensik i Androidbaserade mobila enheter samt så upplyses det om dagensanti-forensiska attack metoder mot forensiska verktyg. Genom experiment så vi-sas det hur man kan förhindra ett forensisk verktyg från att extrahera data medanvändning av ett simpelt script. / Mobile phones have become essential for the extraction of digital artifacts in foren-sic investigations. Android’s Linux-based operating systems bring greater potentialfor anti-forensic methods, which means that knowledge of anti-forensics is essen-tial to today’s IT forensic investigators. In this study, the effect of anti-forensicson Android-based mobile devices is highlighted, as well as revealing today’s anti-forensic attack methods against forensic tools. By experiment, it is shown how toprevent a forensic tool from extracting data by using a simple script.

This paper explores the statistical properties of microfragment recovery techniques used on NTFS filesystems in the use of digital forensics. A microfragment is the remnant file-data existing in the cluster slack after this file has been overwritten. The total amount of cluster slack is related to the size distribution of the overwriting files as well as to the size of cluster. Experiments have been performed by varying the size distributions of the overwriting files as well as the cluster sizes of the partition. These results are then compared with existing analytical models. / FIVES

Focus of this thesis is on the design and implementation of an application for gathering indicators of compromise from the systems. In the thesis, there is an introduction to the term indicator of compromise and description of commonly used categories. Next, there is a summary of existing tools with a similar focus. In the thesis, there is a list of some existing formats for sharing of indicators of compromise and selection of format which resulting application uses. After the implementation, application was tested both locally and on infrastructure of cyber exercise.

Supply chains have become targets for hostile cyber actors. Motivations for cyber crimes include intellectual property theft, customer data theft and industrial espionage. The cyber threat landscape in which businesses operate is constantly evolving. The consequences of a successful cyber attack can be devastating for a business. Increasing the resilience of the supply chain in the digital environment is a complex task because the supply chain consists of different organisations with varying levels of cybersecurity defence capability. Orchestrating cybersecurity improvement in a supply chain requires visibility into the security posture of each participating organisation and this is generally lacking. This thesis studies the potential use of blockchain for enhancing the cybersecurity of the supply chain. The study simulates a permissioned blockchain among supply chain members to monitor digital assets important for cybersecurity. The blockchain is analysed to extract insights from the perspective of a supply chain cybersecurity oversight role. The study finds that a blockchain can provide visibility by sharing cybersecurity-related information among supply chain members. It can also provide a digital forensic record for incident response and forensic investigations.

This report aims to draw a comparison between two commercial mobile forensics and recovery tools, Magnet AXIOM and MOBILedit. A thorough look at previously done studies was helpful to know what aspects of the data extractions must be compared and which areas are the most important ones to focus upon. This work focuses on how the data extracted from one tool compares with another and provides comprehensive extraction based on different scenarios, circumstances, and aspects. Performances of both tools are compared based on various benchmarks and criteria. This study has helped establish that MOBILedit has been able to outperform Magnet AXIOM on more data extraction and recovery aspects. It is a comparatively better tool to get your hands on.

IT-relaterad brottslighet ökar lavinartat och Polismyndigheten står inför nya utmaningar i att identifiera gärningsmän. Allt mer mjukvaror och tjänster blir automatiserade och det gäller även mjukvarorna som Polismyndigheten använder sig av. En av utmaningarna är den oerhörda mängd data som måste processas och analyseras i undersökningar och då förutsätts det att verktygen presenterar data med bibehållen integritet. Verktygen som används är nästan alltid tredjepartsmjukvara och då är det viktigt att rätt data plockas ut och att datan är korrekt. Denna studie har som mål att jämföra två mjukvaror i hur de identifierar och presenterar data. Studien görs i samverkan med Polismyndigheten vid Regionalt IT-brottscentrum Väst – Skövde och hoppas inbringa nya insikter och kunskaper i de verktyg som jämförelsen grundas på och med hjälp av kunskaperna kunna värdesätta integriteten hos verktygen. Resultatet som framträder i studien är att verktygen presenterar data med bibehållen integritet. / Cybercrime is on the rise in society and the Swedish Police is facing new challenges in identifying criminals. More tools and services are becoming automated, and this also applies to the tools that the Swedish Police uses. One of the challenges is the enormous amount of data that must be processed and analyzed during investigations. The tools used are always third-party programs and IT-forensics needs to rely on the organization that makes the software. This study aims to evaluate two different tools in how they identify and present artifacts. The study is conducted in collaboration with the Police Authority at the Regional IT Crime Center West - Skövde and hopes to bring new insights and knowledge into the tools on which the comparison is based on and with the help of the knowledge be able to value the integrity of the tools. The result that the study presents is that the tools are presenting data with preserved integrity.

Better methods and tools are needed in the fight against child pornography. This thesis presents a method for file type categorisation of unknown data fragments, a method for reassembly of JPEG fragments, and the requirements put on an artificial JPEG header for viewing reassembled images. To enable empirical evaluation of the methods a number of tools based on the methods have been implemented. The file type categorisation method identifies JPEG fragments with a detection rate of 100% and a false positives rate of 0.1%. The method uses three algorithms, Byte Frequency Distribution (BFD), Rate of Change (RoC), and 2-grams. The algorithms are designed for different situations, depending on the requirements at hand. The reconnection method correctly reconnects 97% of a Restart (RST) marker enabled JPEG image, fragmented into 4 KiB large pieces. When dealing with fragments from several images at once, the method is able to correctly connect 70% of the fragments at the first iteration. Two parameters in a JPEG header are crucial to the quality of the image; the size of the image and the sampling factor (actually factors) of the image. The size can be found using brute force and the sampling factors only take on three different values. Hence it is possible to use an artificial JPEG header to view full of parts of an image. The only requirement is that the fragments contain RST markers. The results of the evaluations of the methods show that it is possible to find, reassemble, and view JPEG image fragments with high certainty.

The growing popularity of instant messaging has led to the conception of several new applications over the span of the past decade. This has opened up an attack surface for cybercriminals to target susceptible app users. GroupMe is a free IM app widely used by students and so far, no comprehensive forensic analysis has been performed to aid forensic practitioners in recovering evidence from GroupMe on smartphones. This research performs a detailed analysis of the digital artifacts left by the app on Android and iOS devices. This was achieved by installing the app on two mobile phones (Samsung Galaxy S7 Edge and iPhone 6), and identifying each artifact created by performing a series of actions in the app ranging from sending texts, to sharing images and documents, along with their location. Using Cellebrite UFED and Magnet AXIOM, a significant number of artifacts were accurately recovered mainly from the “GroupMe.sqlite” and “GroupMe.sqlite-wal” databases. Out of the 335 artifacts populated on the iPhone, 317 were correctly recovered by both UFED and AXIOM, resulting in an accuracy of 94.62%. No GroupMe related artifacts could be recovered from the Android device. This was due to several physical imaging and rooting limitations imposed by the Samsung SM-935A model, which was used during the study.

The increasing availability of easy-to-use end-to-end encrypted messaging applications has made it possible for more people to conduct their conversations privately. This is something that criminals have taken advantage of and it has proven to make digital forensic investigations more difficult as methods of decrypting the data are needed. In this thesis, data from iOS and Windows devices is extracted and analysed, with focus on the application Signal. Even though other operating systems are compatible with the Signal application, such as Android, it is outside the scope of this thesis. The results of this thesis provide access to data stored in the encrypted application Signalwithout the need for expensive analysis tools. This is done by developing and publishing the first open-source script for decryption and parsing of the Signal database. The script is available for anyone at https://github.com/decryptSignal/decryptSignal.

Using a forensic imager to produce a copy of the storage is a common practice. Due to the large volumes of the modern disks, the imaging may impose severe time overhead which ultimately delays the investigation process. We proposed automated disk analysis techniques that precisely identify regions on the disk that contain data. We also developed a high performance imager that produces AFFv3 images at rates exceeding 300MB/s. Using multiple disk analysis strategies we can analyze a disk within a few minutes and yet reduce the imaging time of by many hours. Partial AFFv3 images produced by our imager can be analyzed by existing digital forensics tools, which makes our approach to be easily incorporated into the workflow of practicing forensics investigators. The proposed approach renders feasible in the forensic environments where the time is critical constraint, as it provides significant performance boost, which facilitates faster investigation turnaround times and reduces case backlogs.