Zhenxiao Qi (UC Riverside), Yu Qu (UC Riverside), Heng Yin (UC Riverside)

Memory forensic tools rely on the knowledge of kernel symbols and kernel object layouts to retrieve digital evidence and artifacts from memory dumps. This knowledge is called profile. Existing solutions for profile generation are either inconvenient or inaccurate. In this paper, we propose a logic inference approach to automatically generating a profile directly from a memory dump. It leverages the invariants existing in kernel data structures across all kernel versions and configurations to precisely locate forensics-required fields in kernel objects. We have implemented a prototype named LOGICMEM and evaluated it on memory dumps collected from mainstream Linux distributions, customized Linux kernels with random configurations, and operating systems designed for Android smartphones and embedded devices. The evaluation results show that the proposed logic inference approach is well-suited for locating forensics-required fields and achieves 100% precision and recall for mainstream Linux distributions and 100% precision and 95% recall for customized kernels with random configurations. Moreover, we show that false negatives can be eliminated with improved logic rules. We also demonstrate that LOGICMEM can generate profiles when it is otherwise difficult (if not impossible) for existing approaches, and support memory forensics tasks such as rootkit detection.

View More Papers

Privacy in Urban Sensing with Instrumented Fleets, Using Air...

Ismi Abidi (IIT Delhi), Ishan Nangia (MPI-SWS), Paarijaat Aditya (Nokia Bell Labs), Rijurekha Sen (IIT Delhi)

Read More

Demo #3: I Am Not Afraid of the GPS...

Ali A. Abdallah (UC Irvine), Zaher M. Kassas (UC Irvine) and Chiawei Lee (US Air Force Test Pilot School)

Read More

EMS: History-Driven Mutation for Coverage-based Fuzzing

Chenyang Lyu (Zhejiang University), Shouling Ji (Zhejiang University), Xuhong Zhang (Zhejiang University & Zhejiang University NGICS Platform), Hong Liang (Zhejiang University), Binbin Zhao (Georgia Institute of Technology), Kangjie Lu (University of Minnesota), Raheem Beyah (Georgia Institute of Technology)

Read More

MIRROR: Model Inversion for Deep LearningNetwork with High Fidelity

Shengwei An (Purdue University), Guanhong Tao (Purdue University), Qiuling Xu (Purdue University), Yingqi Liu (Purdue University), Guangyu Shen (Purdue University); Yuan Yao (Nanjing University), Jingwei Xu (Nanjing University), Xiangyu Zhang (Purdue University)

Read More