Evidence identification in heterogeneous data using clustering

Digital forensics faces several challenges in examining and analyzing data due to an increasing range of technologies at people\u27s disposal. The investigators find themselves having to process and analyze many systems manually (e.g. PC, laptop, Smartphone) in a single case. Unfortunately, current tools such as FTK and Encase have a limited ability to achieve the automation in finding evidence. As a result, a heavy burden is placed on the investigator to both find and analyze evidential artifacts in a heterogenous environment. This paper proposed a clustering approach based on Fuzzy C-Means (FCM) and K-means algorithms to identify the evidential files and isolate the non-related files based on their metadata. A series of experiments using heterogenous real-life forensic cases are conducted to evaluate the approach. Within each case, various types of metadata categories were created based on file systems and applications. The results showed that the clustering based on file systems gave the best results of grouping the evidential artifacts within only five clusters. The proportion across the five clusters was 100% using small configurations of both FCM and K-means with less than 16% of the non-evidential artifacts across all cases -- representing a reduction in having to analyze 84% of the benign files. In terms of the applications, the proportion of evidence was more than 97%, but the proportion of benign files was also relatively high based upon small configurations. However, with a large configuration, the proportion of benign files became very low less than 10%. Successfully prioritizing large proportions of evidence and reducing the volume of benign files to be analyzed, reduces the time taken and cognitive load upon the investigator

Automated Identification of Digital Evidence across Heterogeneous Data Resources

Digital forensics has become an increasingly important tool in the fight against cyber and computer-assisted crime. However, with an increasing range of technologies at people’s disposal, investigators find themselves having to process and analyse many systems with large volumes of data (e.g., PCs, laptops, tablets, and smartphones) within a single case. Unfortunately, current digital forensic tools operate in an isolated manner, investigating systems and applications individually. The heterogeneity and volume of evidence place time constraints and a significant burden on investigators. Examples of heterogeneity include applications such as messaging (e.g., iMessenger, Viber, Snapchat, and WhatsApp), web browsers (e.g., Firefox and Google Chrome), and file systems (e.g., NTFS, FAT, and HFS). Being able to analyse and investigate evidence from across devices and applications in a universal and harmonized fashion would enable investigators to query all data at once. In addition, successfully prioritizing evidence and reducing the volume of data to be analysed reduces the time taken and cognitive load on the investigator. This thesis focuses on the examination and analysis phases of the digital investigation process. It explores the feasibility of dealing with big and heterogeneous data sources in order to correlate the evidence from across these evidential sources in an automated way. Therefore, a novel approach was developed to solve the heterogeneity issues of big data using three developed algorithms. The three algorithms include the harmonising, clustering, and automated identification of evidence (AIE) algorithms. The harmonisation algorithm seeks to provide an automated framework to merge similar datasets by characterising similar metadata categories and then harmonising them in a single dataset. This algorithm overcomes heterogeneity issues and makes the examination and analysis easier by analysing and investigating the evidential artefacts across devices and applications based on the categories to query data at once. Based on the merged datasets, the clustering algorithm is used to identify the evidential files and isolate the non-related files based on their metadata. Afterwards, the AIE algorithm tries to identify the cluster holding the largest number of evidential artefacts through searching based on two methods: criminal profiling activities and some information from the criminals themselves. Then, the related clusters are identified through timeline analysis and a search of associated artefacts of the files within the first cluster. A series of experiments using real-life forensic datasets were conducted to evaluate the algorithms across five different categories of datasets (i.e., messaging, graphical files, file system, internet history, and emails), each containing data from different applications across different devices. The results of the characterisation and harmonisation process show that the algorithm can merge all fields successfully, with the exception of some binary-based data found within the messaging datasets (contained within Viber and SMS). The error occurred because of a lack of information for the characterisation process to make a useful determination. However, on further analysis, it was found that the error had a minimal impact on subsequent merged data. The results of the clustering process and AIE algorithm showed the two algorithms can collaborate and identify more than 92% of evidential files.HCED Ira