Investigation of IndexedDB Persistent Storage for Digital Forensics

The dependency on electronic services is increasing at a rapid rate in every aspect of our daily lives. While the Covid-19 virus remolded how we conduct business through remote collaboration applications, social media is rooting its grasp more in our day-in and day-out activities. Every day, a substantial amount of data is left in both desktop and web-based applications. As the size and the sophistication of stored data increases, so does the complexity of the technology that handles it. Consequently, forensic investigators are facing challenges in constantly adapting to emerging technologies. Hence, these technologies constitute the base for handling the vast size and volume of data in the modern era of information technology. In the scope of this dissertation the efficacy of emerging client-side technology, namely IndexedDB, is scrutinized for forensic value, practices of extraction, processing, presentation, and verification. Accordingly, a series of single case pretest-posttest quasi experiments are conducted to populate artifacts in the underlying storage technologies of IndexedDB. Subsequently, the populated artifacts are extracted and processed based on signature patterns and evaluated for their significance. Additionally, the artifacts are characterized, verified, and presented with the help of cornerstone tools that are implemented in this scope. Furthermore, time-frame analysis is constructed where it is possible to display ordered sequences of events for investigators in a suitable format