Xiaolu Zhang

TL;DR: This research provides findings tactically useful to forensic investigators, not only from the perspective of what data can be obtained, but also important information about which device they should target for acquisition and investigation to obtain the most investigatively useful information.

TL;DR: An automated knowledge-sharing forensic platform that automatically suggests forensic artifact schemas, derived from case data, but does not include any sensitive data in the final (shared) schema is presented.

TL;DR: This work presents case studies and results from analyzing 18 Android vault applications by reverse engineering them and examining the forensic artifacts they produce, and implements a swap attack on 5 18 applications where they achieved unauthorized access to the data by swapping the files that contained the password with a self-created one.

TL;DR: RAPID is presented which is an open source and easy-to-use JAVA library for parsing DEX files and comes with well-documented APIs which allow users to query data directly from the DEX binary files.

TL;DR: A primary account of frameup, an incriminatory attack made possible because of existing implementations in distributed peer to peer storage systems that embrace blockchain and cryptocurrency tokens, and two frameup attacks, both of which take advantage of Storj's implementation.