DeepMem: Learning Graph Neural Network Models for Fast and Robust Memory Forensic Analysis

Explore DeepMem, a groundbreaking graph-based deep learning approach for fast and robust memory forensic analysis, presented in this 26-minute conference talk. Dive into the innovative memory graph model that reconstructs content and topology information from raw memory dumps. Learn about the graph neural network architecture used for node embedding and the object detection method employing cross-validation of evidence. Discover how DeepMem addresses limitations in existing techniques, offering improved detection accuracy, robustness, and efficiency. Gain insights into the voting scheme used for object detection and understand the potential impact of this research on the field of memory forensics.

Intro
Memory Forensics
Existing Techniques
Limitations
Overview of DeepMem
Memory Graph
Embedding Network
Object Detection Basic idea: voting scheme.
Detection Accuracy
Robustness
Efficiency