A framework for the forensic investigation of unstructured email relationship data

Our continued reliance on email communications ensures that it remains a major source of evidence during a digital investigation. Emails comprise both structured and unstructured data. Structured data provides qualitative information to the forensics examiner and is typically viewed through existing tools. Unstructured data is more complex as it comprises information associated with social networks, such as relationships within the network, identification of key actors and power relations, and there are currently no standardised tools for its forensic analysis. Moreover, email investigations may involve many hundreds of actors and thousands of messages. This paper posits a framework for the forensic investigation of email data. In particular, it focuses on the triage and analysis of unstructured data to identify key actors and relationships within an email network. This paper demonstrates the applicability of the approach by applying relevant stages of the framework to the Enron email corpus. The paper illustrates the advantage of triaging this data to identify (and discount) actors and potential sources of further evidence. It then applies social network analysis techniques to key actors within the data set. This paper posits that visualisation of unstructured data can greatly aid the examiner in their analysis of evidence discovered during an investigation

Analysis of roles and position of mobile network operators in mobile payment infrastructure

A number of different mobile payment solutions have been presented the last decade. The phone subscription with its security mechanisms are used for user identification and payments. This is the case for SMS based payment and ticketing systems that are getting more and more popular. However, there are other ways to implement a Trusted Element (TE) , where a SIM card architecture is only one. It can be in the mobile phone, as a separate integrated circuit, as an optional customer deployed plug-in device (e.g., microSD) or be running as an application on a server existing entirely as software. In this paper we analyze what roles and responsibilities different actors have in different types of mobile payments solutions. The main focus is on the implications for the mobile operator business. It turns out that new types of intermediary actors in most cases play an important role. Sometimes mobile operators are not even involved. The emergence of new payment together with other non-SIM card based TE solutions opens up for many different market scenarios for mobile payment services. --

Modeling social networks from sampled data

Network models are widely used to represent relational information among interacting units and the structural implications of these relations. Recently, social network studies have focused a great deal of attention on random graph models of networks whose nodes represent individual social actors and whose edges represent a specified relationship between the actors. Most inference for social network models assumes that the presence or absence of all possible links is observed, that the information is completely reliable, and that there are no measurement (e.g., recording) errors. This is clearly not true in practice, as much network data is collected though sample surveys. In addition even if a census of a population is attempted, individuals and links between individuals are missed (i.e., do not appear in the recorded data). In this paper we develop the conceptual and computational theory for inference based on sampled network information. We first review forms of network sampling designs used in practice. We consider inference from the likelihood framework, and develop a typology of network data that reflects their treatment within this frame. We then develop inference for social network models based on information from adaptive network designs. We motivate and illustrate these ideas by analyzing the effect of link-tracing sampling designs on a collaboration network.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS221 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org