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Design and Implementation of a ML-based Detection System for Malicious Script Hidden Corrupted Digital Files

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2023, 9(6), pp.1-9
  • DOI : 10.20465/KIOTS.2023.9.6.001
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : September 17, 2023
  • Accepted : November 24, 2023
  • Published : December 29, 2023

Lee, Hyung Woo 1 Sangwon Na 1

1한신대학교

Accredited

ABSTRACT

Malware files containing concealed malicious scripts have recently been identified within MS Office documents frequently. In response, this paper describes the design and implementation of a system that automatically detects malicious digital files using machine learning techniques. The system is proficient in identifying malicious scripts within MS Office files that exploit the OLE VBA macro functionality, detecting malicious scripts embedded within the CDH/LFH/ECDR internal field values through OOXML structure analysis, and recognizing abnormal CDH/LFH information introduced within the OOXML structure, which is not conventionally referenced. Furthermore, this paper presents a mechanism for utilizing the VirusTotal malicious script detection feature to autonomously determine instances of malicious tampering within MS Office files. This leads to the design and implementation of a machine learning-based integrated software. Experimental results confirm the software's capacity to autonomously assess MS Office file’s integrity and provide enhanced detection performance for arbitrary MS Office files when employing the optimal machine learning model.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.