loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Colby B. Parker ; J. Todd McDonald and Dimitrios Damopoulos

Affiliation: Department of Computer Science, University of South Alabama, Mobile, AL, U.S.A.

Keyword(s): Software Protection, MATE Attacks, Code Visualization, Neural Networks, Obfuscation.

Abstract: As the need for new techniques to analyze obfuscated software has grown, recent work has shown the ability to analyze programs via machine learning in order to perform automated metadata recovery. Often these techniques really on disassembly or other means of direct code analysis. We showcase an approach combining code visualization and image analysis via convolutional neural networks capable of statically classifying obfuscation transformations. By first turning samples into gray scale images, we are able to analyze the structure and side effects of transformations used in the software with no heavy code analysis or feature preparation. With experimental results samples produced with the Tigress and OLLVM obfuscators, our models are capable of labeling transformations with F1-scores between 90% and 100% across all tests. We showcase our approach via models designed as both a binary classification problem as well as a multi label and multi output problem. We retain high performance e ven in the presence of multiple transformations in a file. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.140.188.157

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Parker, C.; McDonald, J. and Damopoulos, D. (2021). Machine Learning Classification of Obfuscation using Image Visualization. In Proceedings of the 18th International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-524-1; ISSN 2184-7711, SciTePress, pages 854-859. DOI: 10.5220/0010607408540859

@conference{secrypt21,
author={Colby B. Parker. and J. Todd McDonald. and Dimitrios Damopoulos.},
title={Machine Learning Classification of Obfuscation using Image Visualization},
booktitle={Proceedings of the 18th International Conference on Security and Cryptography - SECRYPT},
year={2021},
pages={854-859},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010607408540859},
isbn={978-989-758-524-1},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Security and Cryptography - SECRYPT
TI - Machine Learning Classification of Obfuscation using Image Visualization
SN - 978-989-758-524-1
IS - 2184-7711
AU - Parker, C.
AU - McDonald, J.
AU - Damopoulos, D.
PY - 2021
SP - 854
EP - 859
DO - 10.5220/0010607408540859
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic