Computer Forensics is mainly about investigating crimes where computers has been involved. There are many tools available to aid the investigator with this task. We have created a prototype of a completely new type of tool where all evidences are indexed by its time variable and plotted on a timeline. We believed that this way would make it easier and more intuitive to find coherent evidence and would make it faster to work with for the investigator. We have performed a user test where a group of people has evaluated our prototype tool against a modern commercial computer forensic tool and the results of this test are much better than we expected. The results show that users completed the task much faster and that the results were more correct. They also experienced that the prototype were more intuitive to use and that it was easier to find evidence that was coherent in time.

With the growing popularity of using smartphones in business environments, it is increasingly likely that phones will be the target of attacks and sources of evidence in cyber forensic investigations. It will often be important to identify who was using the phone at the time an incident occurred. This can be very difficult as phones are easily misplaced, borrowed, or stolen. Previous research has attempted to find ways to identify computer users based on behavioral analysis. Current research into user profiling requires highly invasive examinations of potentially sensitive user data that the user might not be comfortable with people inspecting or could be against company policy to store. This study developed user profiles based on changes in a mobile phone's underlying block structure. By examining where and when changes occur, a user profile can be developed that is comparable to more traditional intrusion detection models, but without the need to use invasive data sets. These profiles can then be used to determine user masquerading efforts or detect when a compromise has occurred. This study included 35 participants that used Samsung Galaxy S3s for three months. The results of the study show that this method has a high accuracy of classifying a phone's actual sessions correctly when using 2-class models. Results from the 1-class models were not as accurate, but the Sigmoid SVM was able to correctly classify actual user sessions from attack sessions. <br>

abstract: Cyber threats are growing in number and sophistication making it important to continually study and improve all dimensions of cyber defense. Human teamwork in cyber defense analysis has been overlooked even though it has been identified as an important predictor of cyber defense performance. Also, to detect advanced forms of threats effective information sharing and collaboration between the cyber defense analysts becomes imperative. Therefore, through this dissertation work, I took a cognitive engineering approach to investigate and improve cyber defense teamwork. The approach involved investigating a plausible team-level bias called the information pooling bias in cyber defense analyst teams conducting the detection task that is part of forensics analysis through human-in-the-loop experimentation. The approach also involved developing agent-based models based on the experimental results to explore the cognitive underpinnings of this bias in human analysts. A prototype collaborative visualization tool was developed by considering the plausible cognitive limitations contributing to the bias to investigate whether a cognitive engineering-driven visualization tool can help mitigate the bias in comparison to off-the-shelf tools. It was found that participant teams conducting the collaborative detection tasks as part of forensics analysis, experience the information pooling bias affecting their performance. Results indicate that cognitive friendly visualizations can help mitigate the effect of this bias in cyber defense analysts. Agent-based modeling produced insights on internal cognitive processes that might be contributing to this bias which could be leveraged in building future visualizations. This work has multiple implications including the development of new knowledge about the science of cyber defense teamwork, a demonstration of the advantage of developing tools using a cognitive engineering approach, a demonstration of the advantage of using a hybrid cognitive engineering methodology to study teams in general and finally, a demonstration of the effect of effective teamwork on cyber defense performance. / Dissertation/Thesis / Doctoral Dissertation Applied Psychology 2014

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.

WhatsApp is the most popular instant messaging application worldwide. Since 2016, users can send and receive messages through desktop clients, either through the WhatsApp desktop application or the web client accessible from supported web browsers. The author identified a gap in the literature in terms of WhatsApp forensics for desktop and web clients. The aim of the study was to locate forensic artifacts on WhatsApp clients. These clients included the desktop application on both Windows and Mac operating systems. Chrome and Firefox web clients were also analyzed for the Windows operating system, as well as Chrome and Safari web clients on the Mac operating system. A WhatsApp log file was identified as the main artifact providing information throughout all clients analyzed. Cached profile pictures were also found, as well as history information about visited websites and ran applications.

<div>With the increased evolution in technology over the past decade, there has been a gradual inclination towards utilizing advanced tools, like location-based applications which incorporate features such as constant route or traffic updates with Global Positioning System (GPS), among</div><div>others, which aid in smooth living. Such applications gain access to private information of users, among their other life hack qualities, thus producing a highly vulnerable ground for data exposure such as current location. With the increase in mobile application-based attacks, there exists a</div><div>constant threat scenario in terms of criminal activities which pose an ultimate challenge while tackling large amount of data. This research primarily focuses on the extent of user-specific data that can be obtained while forensically collecting and analysing data from Waze and HEREwego</div><div>applications on Android and iOS platforms. In order to address the lack of forensic research on the above mentioned applications, an in-depth forensic analysis is conducted in this study, utilizing Cellebrite, a professional tool to provide and verify the evidence acquired, that aid in any digital forensic investigations. On the Waze application, 12 artifacts were populated on the Android device and 17 artifacts on the iOS device, out of which 12 artifacts were recovered from the Android device (100% of the artifacts populated) and 12 artifacts from the iOS device (70.58% of the artifacts populated). Similarly on the HEREwego application, 14 artifacts were populated on the Android device and 13 artifacts on the iOS device, out of which 7 artifacts were recovered from the Android device (50% of the artifacts populated) and 7 artifacts from iOS device (53.84% of the artifacts populated).</div>

Mobile applications are an important but fast changing piece of the digital forensics’ world. For mobile forensics researchers and field analysts, it is hard to keep up with the pace of the ever-changing world of the newest and most popular applications teens are using. Mobile forensic tools are quickly becoming more and more supportive of new applications, but with how quickly apps are changing and new ones being released, it is still difficult for the tools to keep up. The research question for this project examines to what extent digital forensic tools support new and emerging applications seen recently in investigations involving teenagers? For this research, a survey was conducted asking digital forensic analysts, and others who investigate digital crimes, what applications they are coming across most frequently during investigations involving teens and whether those applications are being supported by forensic tools. The top three applications from the survey that were not supported by mobile forensic tools, Monkey, Houseparty, and Likee were populated onto a test device and then evaluated and analyzed to see what forensic artifacts were found in those applications. The mobile application artifacts were then compared on two different forensic tools to see which tool obtains the most forensic artifacts from the applications. Through the examination and analysis of the applications and data contained within the apps, it was determined that 61% of the populated forensic artifacts were recovered manually and only 45% were recovered by a forensic tool for the Monkey application. 100% of the populated forensic artifacts were recovered manually and only 29% were recovered by a forensic tool for the Houseparty application. 42% of the populated forensic artifacts were recovered manually and only 3% were recovered by a forensic tool for the Likee application. It was found that the extent of support from digital forensic tools for these types of applications depends greatly on how the application stores the artifacts, but the artifact extraction support was limited for all applications. This research benefits in helping researchers and analysts by understanding the data and artifacts contained within the applications, what forensic artifacts are recoverable, and where to find those important artifacts. This research can help in finding important evidence for future investigations.<br>

<p>Technological advances have a profound impact on people and the world in which they live. People use a wide range of smart devices, such as the Internet of Things (IoT), smartphones, and wearable devices, on a regular basis, all of which store and use location data. With this explosion of technology, these devices have been playing an essential role in digital forensics and crime investigations. Digital forensic professionals have become more able to acquire and assess various types of data and locations; therefore, location data has become essential for responders, practitioners, and digital investigators dealing with digital forensic cases that rely heavily on digital devices that collect data about their users. It is very beneficial and critical when performing any digital/cyber forensic investigation to consider answering the six Ws questions (i.e., who, what, when, where, why, and how) by using location data recovered from digital devices, such as where the suspect was at the time of the crime or the deviant act. Therefore, they could convict a suspect or help prove their innocence. However, many digital forensic standards, guidelines, tools, and even the National Institute of Standards and Technology (NIST) Cyber Security Personnel Framework (NICE) lack full coverage of what location data can be, how to use such data effectively, and how to perform spatial analysis. Although current digital forensic frameworks recognize the importance of location data, only a limited number of data sources (e.g., GPS) are considered sources of location in these digital forensic frameworks. Moreover, most digital forensic frameworks and tools have yet to introduce geo-contextualization techniques and spatial analysis into the digital forensic process, which may aid digital forensic investigations and provide more information for decision-making. As a result, significant gaps in the digital forensics community are still influenced by a lack of understanding of how to properly curate geodata. Therefore, this research was conducted to develop a transdisciplinary framework to deal with the limitations of previous work and explore opportunities to deal with geodata recovered from digital evidence by improving the way of maintaining geodata and getting the best value from them using an iPhone case study. The findings of this study demonstrated the potential value of geodata in digital disciplinary investigations when using the created transdisciplinary framework. Moreover, the findings discuss the implications for digital spatial analytical techniques and multi-intelligence domains, including location intelligence and open-source intelligence, that aid investigators and generate an exceptional understanding of device users' spatial, temporal, and spatial-temporal patterns.</p>