Gordon Russell

Sean McKeown, Gordon Russell

The High Efficiency File Format (HEIF) was adopted by Apple in 2017 as their favoured means of capturing images from their camera application, with Android devices such as the Galaxy S10 providing support more recently. The format is positioned to replace JPEG as the de facto image compression file type, touting many modern features and better compression ratios over the aging standard. However, while millions of devices across the world are already able to produce HEIF files, digital forensics research has not given the format much attention. As HEIF is a complex container format, much different from traditional still picture formats, this leaves forensics practitioners exposed to risks of potentially mishandling evidence. This paper describes the forensically relevant features of the HEIF format, including those which could be used to hide data, or cause issues in an investigation, while also providing commentary on the state of software support for the format. Finally, suggestions for current best-practice are provided, before discussing the requirements of a forensically robust HEIF analysis tool.

Peter McLaren, William J Buchanan, Gordon Russell et al.

Malware writers frequently try to hide the activities of their agents within tunnelled traffic. Within the Kill Chain model the infection time is often measured in seconds, and if the infection is not detected and blocked, the malware agent, such as a bot, will often then set up a secret channel to communicate with its controller. In the case of ransomware the communicated payload may include the encryption key used for the infected host to register its infection. As a malware infection can spread across a network in seconds, it is often important to detect its activities on the air, in memory and at-rest. Malware increasingly uses encrypted channels for communicating with their controllers. This paper presents a new approach to discovering the cryptographic artefacts of real malware clients that use cryptographic libraries of the Microsoft Windows operating system. This enables malware secret communications to be discovered without any prior malware knowledge.

Shane Murnion, William J. Buchanan, Adrian Smales et al.

One major problem with cyberbullying research is the lack of data, since researchers are traditionally forced to rely on survey data where victims and perpetrators self-report their impressions. In this paper, an automatic data collection system is presented that continuously collects in-game chat data from one of the most popular online multi-player games: World of Tanks. The data was collected and combined with other information about the players from available online data services. It presents a scoring scheme to enable identification of cyberbullying based on current research. Classification of the collected data was carried out using simple feature detection with SQL database queries and compared to classification from AI-based sentiment text analysis services that have recently become available and further against manually classified data using a custom-built classification client built for this paper. The simple SQL classification proved to be quite useful at identifying some features of toxic chat such as the use of bad language or racist sentiments, however the classification by the more sophisticated online sentiment analysis services proved to be disappointing. The results were then examined for insights into cyberbullying within this game and it was shown that it should be possible to reduce cyberbullying within the World of Tanks game by a significant factor by simply freezing the player's ability to communicate through the in-game chat function for a short period after the player is killed within a match. It was also shown that very new players are much less likely to engage in cyberbullying, suggesting that it may be a learned behaviour from other players.