BitTorrent Sync: First Impressions and Digital Forensic Implications

With professional and home Internet users becoming increasingly concerned with data protection and privacy, the privacy afforded by popular cloud file synchronisation services, such as Dropbox, OneDrive and Google Drive, is coming under scrutiny in the press. A number of these services have recently been reported as sharing information with governmental security agencies without warrants. BitTorrent Sync is seen as an alternative by many and has gathered over two million users by December 2013 (doubling since the previous month). The service is completely decentralised, offers much of the same synchronisation functionality of cloud powered services and utilises encryption for data transmission (and optionally for remote storage). The importance of understanding BitTorrent Sync and its resulting digital investigative implications for law enforcement and forensic investigators will be paramount to future investigations. This paper outlines the client application, its detected network traffic and identifies artefacts that may be of value as evidence for future digital investigations.Comment: Proc. of Digtial Forensics Research Workshop (DFRWS EU 2014

Analysis of the ‘Db’ Windows Registry Data Structure

The Windows Registry stores a wide variety of data representing a host of different user properties, settings and program information. The data structures used by the registry are designed to be adaptable to store these differences in a simple format. In this paper we will highlight the existence of a rare data structure that is used to store a large amount of data within the registry hives. We analyse the manner in which this data structure stores its data and the implications that it may have on evidence retrieval and digital investigation. In particular, we reveal that the three of the most popular digital investigation suites fail to recognise this structure and do not allow the investigator to view the contents of the structure. Keywords: Windows Registry, Data Structur

BitTorrent Sync: Network Investigation Methodology

The volume of personal information and data most Internet users find themselves amassing is ever increasing and the fast pace of the modern world results in most requiring instant access to their files. Millions of these users turn to cloud based file synchronisation services, such as Dropbox, Microsoft Skydrive, Apple iCloud and Google Drive, to enable "always-on" access to their most up-to-date data from any computer or mobile device with an Internet connection. The prevalence of recent articles covering various invasion of privacy issues and data protection breaches in the media has caused many to review their online security practices with their personal information. To provide an alternative to cloud based file backup and synchronisation, BitTorrent Inc. released an alternative cloudless file backup and synchronisation service, named BitTorrent Sync to alpha testers in April 2013. BitTorrent Sync's popularity rose dramatically throughout 2013, reaching over two million active users by the end of the year. This paper outlines a number of scenarios where the network investigation of the service may prove invaluable as part of a digital forensic investigation. An investigation methodology is proposed outlining the required steps involved in retrieving digital evidence from the network and the results from a proof of concept investigation are presented.Comment: 9th International Conference on Availability, Reliability and Security (ARES 2014

Extraction of User Activity through Comparison of Windows Restore Points

The extraction of past user activity is one of the main goals in the analysis of digital evidence. In this paper we present a methodology for extracting this activity by comparing multiple Restore Points found in the Windows XP operating system. We concentrate on comparing the copies of the registry hives found within these points. The registry copies represent a snapshot in time of the state of the system. Differences between them can reveal user activity from one instant to another. This approach is implemented and presented as a tool that is able to compare any set of offline hive files and present the results to the user. Investigative techniques are presented to use the software as efficiently as possible. The techniques range from general analysis, in which areas of high user activity are pinpointed, to specific techniques, where user activity relating to specific files and file types is found

Machine Learning-based Nutrient Application's Timeline Recommendation for Smart Agriculture: A Large-Scale Data Mining Approach

This study addresses the vital role of data analytics in monitoring fertiliser applications in crop cultivation. Inaccurate fertiliser application decisions can lead to costly consequences, hinder food production, and cause environmental harm. We propose a solution to predict nutrient application by determining required fertiliser quantities for an entire season. The proposed solution recommends adjusting fertiliser amounts based on weather conditions and soil characteristics to promote cost-effective and environmentally friendly agriculture. The collected dataset is high-dimensional and heterogeneous. Our research examines large-scale heterogeneous datasets in the context of the decision-making process, encompassing data collection and analysis. We also study the impact of fertiliser applications combined with weather data on crop yield, using the winter wheat crop as a case study. By understanding local contextual and geographic factors, we aspire to stabilise or even reduce the demand for agricultural nutrients while enhancing crop development. The proposed approach is proven to be efficient and scalable, as it is validated using a real-world and large dataset.Comment: Research articles have: 6 Pages, 6 Figures, and 3 Tables | ACKNOWLEDGMENT: CONSUS is funded under Science Foundation Ireland's Strategic Partnerships Programme (16/SPP/3296) and is co-funded by Origin Enterprises Pl

Extraction and Categorisation of User Activity from Windows Restore Points

The extraction of the user activity is one of the main goals in the analysis of digital evidence. In this paper we present a methodology for extracting this activity by comparing multiple Restore Points found in the Windows XP operating system. The registry copies represent a snapshot of the state of the system at a certain point in time. Differences between them can reveal user activity from one instant to another. The algorithms for comparing the hives and interpreting the results are of high complexity. We develop an approach that takes into account the nature of the investigation and the characteristics of the hives to reduce the complexity of the comparison and result interpretation processes. The approach concentrates on hives that present higher activity and highlights only those differences that are relevant to the investigation. The approach is implemented as a software tool that is able to compare any set of offline hives and categorise the results according to the user needs. The categorisation of the results, in terms of activity will help the investigator in interpreting the results. In this paper we present a general concept of result categorisation to prove its efficiency on Windows XP, but these can be adapted to any Windows versions including the latest versions