LogGC

LogGC: garbage collecting audit log. System-level audit logs capture the interactions between applications and the runtime environment. They are highly valuable for forensic analysis that aims to identify the root cause of an attack, which may occur long ago, or to determine the ramifications of an attack for recovery from it. A key challenge of audit log-based forensics in practice is the sheer size of the log files generated, which could grow at a rate of Gigabytes per day. In this paper, we propose LogGC, an audit logging system with garbage collection (GC) capability. We identify and overcome the unique challenges of garbage collection in the context of computer forensic analysis, which makes LogGC different from traditional memory GC techniques. We also develop techniques that instrument user applications at a small number of selected places to emit additional system events so that we can substantially reduce the false dependences between system events to improve GC effectiveness. Our results show that LogGC can reduce audit log size by 14 times for regular user systems and 37 times for server systems, without affecting the accuracy of forensic analysis.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element


References in zbMATH (referenced in 1 article )

Showing result 1 of 1.
Sorted by year (citations)

  1. Omid Setayeshfar, Christian Adkins, Matthew Jones, Kyu Hyung Lee, Prashant Doshi: GrAALF: Supporting graphical analysis of audit logs for forensics (2021) not zbMATH