Design and Implementation of an Automatic Detection Mechanism for Malicious Information Based on Macro Data in MS-Office File (MS-Office 계열 파일 내 매크로 정보 기반 악성 정보 자동 검출 메커니즘 설계 및 구현)

Hyung-Woo Lee (이형우)

doi:10.20465/KIOTS.2025.11.1.107

Design and Implementation of an Automatic Detection Mechanism for Malicious Information Based on Macro Data in MS-Office File

Journal of Internet of Things and Convergence
Abbr : JKIOTS
2025, 11(1), pp.107~114
DOI : 10.20465/KIOTS.2025.11.1.107
Publisher : The Korea Internet of Things Society
Research Area : Engineering > Computer Science > Internet Information Processing
Received : January 21, 2025
Accepted : February 17, 2025
Published : February 28, 2025

Hyung-Woo Lee ¹

¹한신대학교

Accredited

ABSTRACT

In this paper, we designed and implemented a system that applies machine learning techniques to automatically detect malicious macro information embedded in MS Office files. When an MS Office file contains macro functionality, it may perform abnormal actions on the user’s system, potentially leading to the leakage of sensitive personal information stored within the system. Therefore, a system is needed to automatically detect malicious scripts embedded in MS Office files and verify any tampering. To achieve this, this study applies supervised and unsupervised learning-based machine learning models to design and implement an automatic detection system for determining whether an MS Office file contains malicious macro data. Experimental results demonstrate that the proposed system provides improved detection performance compared to existing methods, ensuring a safer environment for the use of MS Office files.

KEYWORDS

MS Office Macro File, Malicious Code, Machine Learning, Auto-Detection System, Supervised/Unsupervised Learning Model

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* References for papers published after 2025 are currently being built.

[journal] S. Rafique / 2021 / Macro Malware: Detection and Mitigation Techniques / Journal of Cyber Security 12(3) : 45~60

[confproc] S. D. l. Santos / 2017 / Macro malware detection using machine learning techniques - a new approach / Proceedings of the 3rd International Conference on Information SystemsSecurity and Privacy - Volume 1: ICISSP, INSTICC / SciTePress : 295~302

[journal] P. Sung / 2022 / Behavioral Analysis of Macro-based Malware / IEEE Transactions on Information Security and Forensics 17(2) : 299~312

[journal] A. Patel / 2020 / Office Macro Malware: How Attackers Exploit VBA for Cybercrime / Cyber Threat Research Journal 8(1) : 11~25

[book] J. Saxe / 2018 / Malware Data Science: Attack Detection and Attribution / No Starch Press

[confproc] T. Schmid / 2020 / Machine Learning for Malware Detection / Proceedings of the IEEE International Conference on Machine Learning and Security (ICMLS)

[other] S. Zeltser / 2019 / Understanding Static and Dynamic Malware Analysis Techniques

[confproc] C. Szegedy / 2019 / Intrusion Detection Using Machine Learning Techniques / ACM Conference on Cybersecurity and AI

[thesis] P. Singh / 2017 / Detection of Malicious OOXML Documents Using Domain Specific Features / Master's thesis / Indian Institute of Information Technology and Management

[journal] Z. Wang / 2021 / Applications of Machine Learning in Public Security Informatin and Resource Management / Hindawi Scientific Programming 2021 : 4734187~

[journal] R. Gupta / 2020 / Machine learning models for secure data analytics: a taxonomy and threat model / Computer Communications 153 : 406~440

[journal] A. Cohen / 2016 / Sfem: Structural feature extraction methodology for the detection of malicious office documents using machine learning methods / ExpertSystems with Applications 63 : 324~343

[journal] H. S. Lee / 2019 / Forgery Detection Mechanism with Abnormal Structure Analysis on Office Open XML based MS-Word File / IJASC 8(4)

[confproc] S. Na / 2023 / Implementation of Malicious Data Analysis and Detection System Hidden in the Slack Space of Corrupted OOXML-based MS-Office Digital Files / 9th International Joint Conference on Convergence, IJCC2023 21 : 97~103

[journal] A. Catsiglione / 2011 / Hiding Information into OOXML Documents: New Steganographic Perspectives / Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 2(4) : 59~83

[confproc] S. Kim / 2018 / Obfuscated vba macro detection using machine learning / 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) : 490~501

[journal] B. Mahesh / 2020 / Machine Learning Algorithms - A Review / International Journal of Science and Research 9(1)

[book] W. Richert / Building Machine Learning Systems with Python / Packt Publishing Ltd

[web] VirusTotal / VirusTotal / VirusTotal / https://www.virustotal.com/

[web] / Python / https://www.python.org/

[web] / scikit-learn / https://scikit-learn.org/

[web] MalwareBazaar / https://bazaar.abuse.ch/ / https://bazaar.abuse.ch/

[web] / Digital Corpora / Digital Corpora / https://digitalcorpora.org/

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Journal of Internet of Things and Convergence 2025 KCI Impact Factor : 0.75

Design and Implementation of an Automatic Detection Mechanism for Malicious Information Based on Macro Data in MS-Office File

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* References for papers published after 2025 are currently being built.

Journal of Internet of Things and Convergence 2025 KCI Impact Factor : 0.75

Design and Implementation of an Automatic Detection Mechanism for Malicious Information Based on Macro Data in MS-Office File

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KEYWORDS

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