Robust PDF Files Forensics Using Coding Style

Adhatarao, Supriya; Lauradoux, Cédric

doi:10.1007/978-3-031-06975-8_11

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…The author introduced a strategy to identify the software that created a PDF file [41] based on coding style: specific patterns that specific PDF producers only produce. Additionally, they looked at the coding practices of 900 PDF files created by 11 distinct PDF producers on three different operating systems.…”

Section: Literature Reviewmentioning

confidence: 99%

PDF Malware Detection Based on Optimizable Decision Trees

2022

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Portable document format (PDF) files are one of the most universally used file types. This has incentivized hackers to develop methods to use these normally innocent PDF files to create security threats via infection vector PDF files. This is usually realized by hiding embedded malicious code in the victims’ PDF documents to infect their machines. This, of course, results in PDF malware and requires techniques to identify benign files from malicious files. Research studies indicated that machine learning methods provide efficient detection techniques against such malware. In this paper, we present a new detection system that can analyze PDF documents in order to identify benign PDF files from malware PDF files. The proposed system makes use of the AdaBoost decision tree with optimal hyperparameters, which is trained and evaluated on a modern inclusive dataset, viz. Evasive-PDFMal2022. The investigational assessment demonstrates a lightweight and accurate PDF detection system, achieving a 98.84% prediction accuracy with a short prediction interval of 2.174 μSec. To this end, the proposed model outperforms other state-of-the-art models in the same study area. Hence, the proposed system can be effectively utilized to uncover PDF malware at a high detection performance and low detection overhead.

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Section: Literature Reviewmentioning

confidence: 99%

PDF Malware Detection Based on Optimizable Decision Trees

2022

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An Enhanced Feature-Based Hybrid Approach for Adversarial PDF Malware Detection

Hossain,

Deb,

Sarker

2024

2024 6th International Conference on Electrical Engineering and Information &Amp;amp; Communication Technology (ICEEICT)

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Forensic authenticity examination of PDF documents

Zeng

Qiu

2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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With the rapid development and widespread popularization of information technology, Portable Document Format (PDF) documents have gradually become a type of digital files that are easily accessible and closely related to the public. In digital forensics, digital files in the form of PDF documents are often encountered, and their authenticity needs to be determined. Therefore, the research on its key forensic techniques has important theoretical research significance and practical application value. However, through searching the literature, we can find that there is still a lack of systematic research on forensic authentication of PDF documents. Based on the above situation, this paper first studies the file structure and digital composition of PDF documents; The digital data characteristics and the contents for examination of PDF files produced by the mainstream scenarios are studied, including PDF generated by scanners, converting directly from images, and transforming from DOCX documents, etc. Finally, a case study is carried out to explain the key technologies and examination contents for authenticity examinations of PDF documents in detail. Our study will provide theoretical basis and practical guidance for forensic investigations of authenticity examinations of PDF documents.

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Robust PDF Files Forensics Using Coding Style

Cited by 3 publications

References 15 publications

PDF Malware Detection Based on Optimizable Decision Trees

PDF Malware Detection Based on Optimizable Decision Trees

An Enhanced Feature-Based Hybrid Approach for Adversarial PDF Malware Detection

Forensic authenticity examination of PDF documents

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