Annual Computer Security Applications Conference (ACSAC) 2020

Full Program »

App-Agnostic Post-Execution Semantic Analysis of Android In-Memory Forensics Artifacts

Over the last decade, userland memory forensics techniques and algorithms have gained popularity among practitioners, as they have proven to be useful in real forensics and cybercrime investigations. These techniques analyze and recover objects and artifacts from process memory space that are of critical importance in investigations. Nonetheless, the major drawback of existing techniques is that they cannot determine the origin and context within which the recovered object exists without prior knowledge of the application logic.

Thus, in this research, we present a solution to close the gap between application-specific and application-generic techniques. We introduce OAGen, a post-execution and app-agnostic semantic analysis approach designed to help investigators establish concrete evidence by identifying the provenance and relationships between in-memory objects in a process memory image. OAGen utilizes Points-to analysis to reconstruct a runtime’s object allocation network. The resulting graph is then fed as an input into our semantic analysis algorithms to determine objects’ origin, context, and scope in the network. The results of our experiments exhibit OAGen’s ability to effectively create an allocation network even for memory-intensive applications with thousands of objects, like Facebook. The performance evaluation of our approach across fourteen different Android apps shows OAGen can efficiently search and decode nodes, and identify their references with a modest throughput rate. Further practical application of OAGen demonstrated in two case studies shows that our approach can aid investigators in the recovery of deleted messages and the detection of malware functionality in post-execution program analysis.

Aisha Ali-Gombe
Towson University

Alexandra Tambaoan
Towson University

Angela Gurfolino
Towson University

Golden G. Richard III
Louisiana State University

Paper (ACM DL)




Powered by OpenConf®
Copyright©2002-2021 Zakon Group LLC