PhD research

(PhD research and others, like EC projects – controlled by the individuals)

  • Yi-Ching Liao (2013-2016): “Process Tracking for Forensic Readiness”, Supervisors: Hanno Langweg, Katrin Franke

Abstact: Forensic analysis suffers from insufficient logging of events, and current system loggers do not record enough information for incident analysis and replay. Similar to flight data recorders preserving performance parameters for aircraft accident investigation, comprehensive process tracking can provide precise, timely, complete, and dependable information for incident investigation and replay. Moreover, the collected traces can recover the traceability links between the incident and the person or action accountable for the incident. We summarise our research on process tracking for forensic readiness, including the state-changing activities of processes, cost-benefit analysis of process tracking, the architecture for process tracking, and privacy implications of process tracking. We consider the admissibility issues as future work.

  • Andrii Shalaginov (2013-2017): “Application of soft computing for cybercrime investigation: an adaptive approach to big data analysis in agile environments”, Supervisors: Katrin Franke, Slobodan Petrovic

Abstract: The cybercrime investigations are influenced by huge amount data due to the widely adoption of information and communication technologies. The volume, the velocity, the variety and the complexity of data have became so high that the contemporary data mining approaches are no more efficient for use in forensic data science. Moreover, distributed techniques for data storage are able to handle the size and the velocity of newly generated data. Forensics analysts experience difficulties in forensically sound big-data processing due to lack of corresponding techniques. There are no unique solutions at the moment available and the approaches vary from a case to a case, e.g. they are not reliable. To achieve multiple goals we aim to develop a adaptie computational methods for cybercrime investigation, that are based particularly on the nature-inspired Soft Computing paradigm. This are able to provide explainable solution to computationally hard problems and reduce efforts for manual analysis and perception. Our objective is to apply the hybridisation of the existing solutions, which can overcome known limitations of standalone Soft Computing methods, such as accuracy and computational time.

  • Ambika Shrestha Chitrakar (2014-2017): “Approximate search techniques for big data analysis”, Supervisors: Slobodan Petrovic, Katrin Franke

Abstract: Approximate search is a process of finding the occurrences of a search pattern into the search text, allowing k number of errors. Here, errors could be the number of character insertions, deletions, and substitutions depending on the type of distance functions used to measure the differences between two strings. By “Big data”, we usually mean a large data set collected from variety of data sources. Different data sources can have their own standards to represent the same data, due to which there is a high probability of errors in the large data sets. Since exact search does not consider such errors and reports a match, only when there is an exact match between two strings, the exact search algorithms are not effective for the data sets and search patterns with errors. Approximate search plays an important role in such cases. Moreover, currently existing approximate search algorithms may create bottlenecks in the search applications that deal with very large inputs (search patterns), and their performance is more important when the search has to be performed in real time in critical applications. This research aims to reduce the time consumption of currently available approximate search algorithms so that they can become suitable for the dynamic data sets that grow over time.

  • Dmytro Piatkivskyi (2015-2018): “De-anonymization in cryptocurrencies“, Supervisors: Mariusz Nowostawski, Stefan Axelsson,

Abstract: The world of virtual currencies is exponentially growing having ambitions to replace many of the present-day financial systems. Bitcoin, the first de-cenralized cryptocurrency, has hitherto been the most successful one out of many existing virtual currencies. Yet, having many limitations, Bitcoin is continuously being refined. Scalability has been the biggest issue up until now which encouraged an invention of off-chain transactions. The off- chain transactions are Bitcoin transactions that do not get on the Bitcoin’s public ledger, blockchain. Such a concept changes the whole idea in a principle way. The research will descibe what problems de-cenralized cryptocurrencies try to solve, what are the current limitations and approaches to solve them. We will be looking into the methods used to de-anonymise fraudsters in cryptocurrencies such as Bitcoin. Particular attention is to be paid to transactions happening off the chain.