@inproceedings{a2e663f1003e46758f4eee6fa226c833,
title = "DeepMemIntrospect: Recognizing Data Structures in Memory with Neural Networks",
abstract = "One yet-To-be-solved but very vital memory forensic problem is to recover data structure information from a specified memory range. Unlike previous studies relying on fixed signature of value or structure, DeepMemlntrospect is the first convolution neural network (CNN) based memory forensic system that can recover data structure information merely from raw memory without relying on signatures. Our experimental results demonstrate high accuracy with over 99% and also show significant performance improvement.",
keywords = "Memory forensic, data structure reversing, deep learning",
author = "Chen, {Chung Kuan} and Ho, {E. Lin} and Shiuh-Pyng Shieh",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Conference on Dependable and Secure Computing, DSC 2018 ; Conference date: 10-12-2018 Through 13-12-2018",
year = "2019",
month = jan,
day = "23",
doi = "10.1109/DESEC.2018.8625160",
language = "English",
series = "DSC 2018 - 2018 IEEE Conference on Dependable and Secure Computing",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "DSC 2018 - 2018 IEEE Conference on Dependable and Secure Computing",
address = "美國",
}