Detection of Malicious PDF based on Document Structure Features and Stream Object (문서 구조 및 스트림 오브젝트 분석을 통한 문서형 악성코드 탐지)

Ah Reum Kang (강아름); Young-Seob Jeong (정영섭); Se Lyeong Kim (김세령); Jonghyun Kim (김종현); Jiyoung Woo (우지영); Sunoh Choi (최선오)

doi:10.9708/jksci.2018.23.11.085

Detection of Malicious PDF based on Document Structure Features and Stream Object

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2018, 23(11), pp.85-93
DOI : 10.9708/jksci.2018.23.11.085
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : October 1, 2018
Accepted : November 1, 2018
Published : November 30, 2018

Ah Reum Kang ¹, Young-Seob Jeong ¹, Se Lyeong Kim ², Jonghyun Kim ³, Jiyoung Woo ¹, Sunoh Choi ³

¹순천향대학교
²한국인터넷진흥원
³한국전자통신연구원

Accredited

ABSTRACT

In recent years, there has been an increasing number of ways to distribute document-based malicious code using vulnerabilities in document files. Because document type malware is not an executable file itself, it is easy to bypass existing security programs, so research on a model to detect it is necessary. In this study, we extract main features from the document structure and the JavaScript contained in the stream object In addition, when JavaScript is inserted, keywords with high occurrence frequency in malicious code such as function name, reserved word and the readable string in the script are extracted. Then, we generate a machine learning model that can distinguish between normal and malicious. In order to make it difficult to bypass, we try to achieve good performance in a black box type algorithm. For an experiment, a large amount of documents compared to previous studies is analyzed. Experimental results show 98.9% detection rate from three different type algorithms. SVM, which is a black box type algorithm and makes obfuscation difficult, shows much higher performance than in previous studies.

KEYWORDS

malware, PDF, machine learning, java script, detection

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

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Detection of Malicious PDF based on Document Structure Features and Stream Object

ABSTRACT

KEYWORDS

Citation status

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

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Detection of Malicious PDF based on Document Structure Features and Stream Object

ABSTRACT

KEYWORDS

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REFERENCES (13) * References for papers published after 2023 are currently being built.

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