Using smartphones as a proxy for forensic evidence contained in cloud storage services

Cloud storage services such as Dropbox, Box and SugarSync have been embraced by both individuals and organizations. This creates an environment that is potentially conducive to security breaches and malicious activities. The investigation of these cloud environments presents new challenges for the digital forensics community. It is anticipated that smartphone devices will retain data from these storage services. Hence, this research presents a preliminary investigation into the residual artifacts created on an iOS and Android device that has accessed a cloud storage service. The contribution of this paper is twofold. First, it provides an initial assessment on the extent to which cloud storage data is stored on these client-side devices. This view acts as a proxy for data stored in the cloud. Secondly, it provides documentation on the artifacts that could be useful in a digital forensics investigation of cloud services

Recovering Residual Forensic Data from Smartphone Interactions with Cloud Storage Providers

There is a growing demand for cloud storage services such as Dropbox, Box, Syncplicity and SugarSync. These public cloud storage services can store gigabytes of corporate and personal data in remote data centres around the world, which can then be synchronized to multiple devices. This creates an environment which is potentially conducive to security incidents, data breaches and other malicious activities. The forensic investigation of public cloud environments presents a number of new challenges for the digital forensics community. However, it is anticipated that end-devices such as smartphones, will retain data from these cloud storage services. This research investigates how forensic tools that are currently available to practitioners can be used to provide a practical solution for the problems related to investigating cloud storage environments. The research contribution is threefold. First, the findings from this research support the idea that end-devices which have been used to access cloud storage services can be used to provide a partial view of the evidence stored in the cloud service. Second, the research provides a comparison of the number of files which can be recovered from different versions of cloud storage applications. In doing so, it also supports the idea that amalgamating the files recovered from more than one device can result in the recovery of a more complete dataset. Third, the chapter contributes to the documentation and evidentiary discussion of the artefacts created from specific cloud storage applications and different versions of these applications on iOS and Android smartphones

Learning-Based Optimization of Cache Content in a Small Cell Base Station

Optimal cache content placement in a wireless small cell base station (sBS) with limited backhaul capacity is studied. The sBS has a large cache memory and provides content-level selective offloading by delivering high data rate contents to users in its coverage area. The goal of the sBS content controller (CC) is to store the most popular contents in the sBS cache memory such that the maximum amount of data can be fetched directly form the sBS, not relying on the limited backhaul resources during peak traffic periods. If the popularity profile is known in advance, the problem reduces to a knapsack problem. However, it is assumed in this work that, the popularity profile of the files is not known by the CC, and it can only observe the instantaneous demand for the cached content. Hence, the cache content placement is optimised based on the demand history. By refreshing the cache content at regular time intervals, the CC tries to learn the popularity profile, while exploiting the limited cache capacity in the best way possible. Three algorithms are studied for this cache content placement problem, leading to different exploitation-exploration trade-offs. We provide extensive numerical simulations in order to study the time-evolution of these algorithms, and the impact of the system parameters, such as the number of files, the number of users, the cache size, and the skewness of the popularity profile, on the performance. It is shown that the proposed algorithms quickly learn the popularity profile for a wide range of system parameters.Comment: Accepted to IEEE ICC 2014, Sydney, Australia. Minor typos corrected. Algorithm MCUCB correcte

A Guide to Distributed Digital Preservation

This volume is devoted to the broad topic of distributed digital preservation, a still-emerging field of practice for the cultural memory arena. Replication and distribution hold out the promise of indefinite preservation of materials without degradation, but establishing effective organizational and technical processes to enable this form of digital preservation is daunting. Institutions need practical examples of how this task can be accomplished in manageable, low-cost ways."--P. [4] of cove

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

MADServer: An Architecture for Opportunistic Mobile Advanced Delivery

Rapid increases in cellular data traffic demand creative alternative delivery vectors for data. Despite the conceptual attractiveness of mobile data offloading, no concrete web server architectures integrate intelligent offloading in a production-ready and easily deployable manner without relying on vast infrastructural changes to carriers’ networks. Delay-tolerant networking technology offers the means to do just this. We introduce MADServer, a novel DTN-based architecture for mobile data offloading that splits web con- tent among multiple independent delivery vectors based on user and data context. It enables intelligent data offload- ing, caching, and querying solutions which can be incorporated in a manner that still satisfies user expectations for timely delivery. At the same time, it allows for users who have poor or expensive connections to the cellular network to leverage multi-hop opportunistic routing to send and receive data. We also present a preliminary implementation of MADServer and provide real-world performance evaluations

VirusPKT: A Search Tool For Assimilating Assorted Acquaintance For Viruses

Viruses utilize various means to circumvent the immune detection in the biological systems. Several mathematical models have been investigated for the description of viral dynamics in the biological system of human and various other species. One common strategy for evasion and recognition of viruses is, through acquaintance in the systems by means of search engines. In this perspective a search tool have been developed to provide a wider comprehension about the structure and other details on viruses which have been narrated in this paper. This provides an adequate knowledge in evolution and building of viruses, its functions through information extraction from various websites. Apart from this, tool aim to automate the activities associated with it in a self-maintainable, self-sustainable, proactive one which has been evaluated through analysis made and have been discussed in this paper

Privacy Preserving Internet Browsers: Forensic Analysis of Browzar

With the advance of technology, Criminal Justice agencies are being confronted with an increased need to investigate crimes perpetuated partially or entirely over the Internet. These types of crime are known as cybercrimes. In order to conceal illegal online activity, criminals often use private browsing features or browsers designed to provide total browsing privacy. The use of private browsing is a common challenge faced in for example child exploitation investigations, which usually originate on the Internet. Although private browsing features are not designed specifically for criminal activity, they have become a valuable tool for criminals looking to conceal their online activity. As such, Technological Crime units often focus their forensic analysis on thoroughly examining the web history on a computer. Private browsing features and browsers often require a more in-depth, post mortem analysis. This often requires the use of multiple tools, as well as different forensic approaches to uncover incriminating evidence. This evidence may be required in a court of law, where analysts are often challenged both on their findings and on the tools and approaches used to recover evidence. However, there are very few research on evaluating of private browsing in terms of privacy preserving as well as forensic acquisition and analysis of privacy preserving internet browsers. Therefore in this chapter, we firstly review the private mode of popular internet browsers. Next, we describe the forensic acquisition and analysis of Browzar, a privacy preserving internet browser and compare it with other popular internet browser