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Analysis and Detection of Malicious Data Hidden in Slack Space on OOXML-based Corrupted MS-Office Digital Files

첫 페이지 보기
  • 발행기관
    국제인공지능학회(구 한국인터넷방송통신학회) 바로가기
  • 간행물
    The International Journal of Advanced Smart Convergence 바로가기
  • 통권
    Volume 12 Number 1 (2023.03)바로가기
  • 페이지
    pp.149-156
  • 저자
    Sangwon Na, Hyung-Woo Lee
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A427733

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원문정보

초록

영어
OOXML-based MS-Office digital files are extensively utilized by businesses and organizations worldwide. However, OOXML-based MS-Office digital files are vulnerable to forgery and corruption attack by including hidden suspicious information, which can lead to activating malware or shell code being hidden in the file. Such malicious code can cause a computer system to malfunction or become infected with ransomware. To prevent such attacks, it is necessary to analyze and detect the corruption of OOXML-based MS-Office files. In this paper, we examine the weaknesses of the existing OOXML-based MS-Office file structure and analyzes how concealment and forgery are performed on MS-Office digital files. As a result, we propose a system to detect hidden data effectively and proactively respond to ransomware attacks exploiting MS-Office security vulnerabilities. Proposed system is designed to provide reliable and efficient detection of hidden data in OOXML-based MS-Office files, which can help organizations protect against potential security threats.

목차

Abstract
1. Introduction
2. OOXML based MS-Office Series Digital File Structure
3. Hidden Malicious Data on OOXML-based MS-Office Digital File
3.1 Hiding Data on Slack Space of OOXML based MS-Office File
3.2 Data Hiding Methods on OOXML based MS-Office File
3.3 Comparison of Data Hiding Methods on OOXML based MS-Office File
4. Hidden Malicious Data Analysis and Detection on Corrupted MS-Office Files
4.1 Slack Space, Hidden Data and Corrupted CDH Detection
4.2 Implementation and Analysis Results
5. Conclusions
Acknowledgment
References

키워드

Corrupted Digital File OOXML-based MS-Office Slack Space Detection Digital Forensics Ransomware.

저자

  • Sangwon Na [ Bachelor student, Div. of Computer Engineering, Hanshin University, Korea ]
  • Hyung-Woo Lee [ Professor, Div. of Computer Engineering, Hanshin University, Korea ] Corresponding author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    국제인공지능학회(구 한국인터넷방송통신학회) [The International Association for Artificial Intelligence]
  • 설립연도
    2000
  • 분야
    공학>전자/정보통신공학
  • 소개
    인터넷방송, 인터넷 TV , 방송 통신 네트워크 및 관련 분야에 대한 국내는 물론 국제적인 학술, 기술의 진흥발전에 공헌하고 지식 정보화 사회에 기여하고자 한다.

간행물

  • 간행물명
    The International Journal of Advanced Smart Convergence
  • 간기
    계간
  • pISSN
    2288-2847
  • eISSN
    2288-2855
  • 수록기간
    2012~2025
  • 십진분류
    KDC 326 DDC 380

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