abstract: In order to catch the smartest criminals in the world, digital forensics examiners need a means of collaborating and sharing information with each other and outside experts that is not prohibitively difficult. However, standard operating procedures and the rules of evidence generally disallow the use of the collaboration software and techniques that are currently available because they do not fully adhere to the dictated procedures for the handling, analysis, and disclosure of items relating to cases. The aim of this work is to conceive and design a framework that provides a completely new architecture that 1) can perform fundamental functions that are common and necessary to forensic analyses, and 2) is structured such that it is possible to include collaboration-facilitating components without changing the way users interact with the system sans collaboration. This framework is called the Collaborative Forensic Framework (CUFF). CUFF is constructed from four main components: Cuff Link, Storage, Web Interface, and Analysis Block. With the Cuff Link acting as a mediator between components, CUFF is flexible in both the method of deployment and the technologies used in implementation. The details of a realization of CUFF are given, which uses a combination of Java, the Google Web Toolkit, Django with Apache for a RESTful web service, and an Ubuntu Enterprise Cloud using Eucalyptus. The functionality of CUFF's components is demonstrated by the integration of an acquisition script designed for Android OS-based mobile devices that use the YAFFS2 file system. While this work has obvious application to examination labs which work under the mandate of judicial or investigative bodies, security officers at any organization would benefit from the improved ability to cooperate in electronic discovery efforts and internal investigations. / Dissertation/Thesis / M.S. Computer Science 2011

The Internet in its most basic form is a complex information sharing organism. There are billions of interconnected elements with varying capabilities that work together supporting numerous activities (services) through this information sharing. In recent times, these elements have become portable, mobile, highly computationally capable and more than ever intertwined with human controllers and their activities. They are also rapidly being embedded into other everyday objects and sharing more and more information in order to facilitate automation, signaling that the rise of the Internet of Things is imminent. In every human society there are always miscreants who prefer to drive against the common good and engage in illicit activity. It is no different within the society interconnected by the Internet (The Internet Society). Law enforcement in every society attempts to curb perpetrators of such activities. However, it is immensely difficult when the Internet is the playing field. The amount of information that investigators must sift through is incredibly massive and prosecution timelines stated by law are prohibitively narrow. The main solution towards this Big Data problem is seen to be the automation of the Digital Investigation process. This encompasses the entire process: From the detection of malevolent activity, seizure/collection of evidence, analysis of the evidentiary data collected and finally to the presentation of valid postulates. This paper focuses mainly on the automation of the evidence capture process in an Internet of Things environment. However, in order to comprehensively achieve this, the subsequent and consequent procedures of detection of malevolent activity and analysis of the evidentiary data collected, respectively, are also touched upon. To this effect we propose the Live Evidence Information Aggregator (LEIA) architecture that aims to be a comprehensive automated digital investigation tool. LEIA is in essence a collaborative framework that hinges upon interactivity and sharing of resources and information among participating devices in order to achieve the necessary efficiency in data collection in the event of a security incident. Its ingenuity makes use of a variety of technologies to achieve its goals. This is seen in the use of crowdsourcing among devices in order to achieve more accurate malicious event detection; Hypervisors with inbuilt intrusion detection capabilities to facilitate efficient data capture; Peer to Peer networks to facilitate rapid transfer of evidentiary data to a centralized data store; Cloud Storage to facilitate storage of massive amounts of data; and the Resource Description Framework from Semantic Web Technologies to facilitate the interoperability of data storage formats among the heterogeneous devices. Within the description of the LEIA architecture, a peer to peer protocol based on the Bittorrent protocol is proposed, corresponding data storage and transfer formats are developed, and network security protocols are also taken into consideration. In order to demonstrate the LEIA architecture developed in this study, a small scale prototype with limited capabilities has been built and tested. The prototype functionality focuses only on the secure, remote acquisition of the hard disk of an embedded Linux device over the Internet and its subsequent storage on a cloud infrastructure. The successful implementation of this prototype goes to show that the architecture is feasible and that the automation of the evidence seizure process makes the otherwise arduous process easy and quick to perform.