2018
DOI: 10.1007/978-3-319-73951-9_9
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PDF-Malware Detection: A Survey and Taxonomy of Current Techniques

Abstract: Portable Document Format, more commonly known as PDF, has become, in the last twenty years, a standard for document exchange and dissemination due its portable nature and widespread adoption. The flexibility and power of this format are not only leveraged by benign users, but from hackers as well who have been working to exploit various types of vulnerabilities, overcome security restrictions, and then transform the PDF format in one among the leading malicious code spread vectors. Analyzing the content of mal… Show more

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Cited by 23 publications
(16 citation statements)
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(60 reference statements)
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“…Accordingly, we described all machine learning-based solutions for PDF malware detection that have been proposed in the last decade. Notably, this part differentiates from previously proposed surveys, such as the ones by Nissim et al [72] and Elingiusti et al [32], which did not focus on those system components that are crucial to understanding adversarial attacks. For example, our work provided a deep insight into the pre-processing of PDF files, which has been exploited by many adversarial attacks.…”
Section: Discussionmentioning
confidence: 88%
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“…Accordingly, we described all machine learning-based solutions for PDF malware detection that have been proposed in the last decade. Notably, this part differentiates from previously proposed surveys, such as the ones by Nissim et al [72] and Elingiusti et al [32], which did not focus on those system components that are crucial to understanding adversarial attacks. For example, our work provided a deep insight into the pre-processing of PDF files, which has been exploited by many adversarial attacks.…”
Section: Discussionmentioning
confidence: 88%
“…[55,59,78,84,93,94,100,101]). For a more detailed description of such systems, we refer the reader to more general purpose surveys [32,72]. Learning-based PDF malware detection The primary goal of machine-learning detectors for malicious documents is discriminating between benign and malicious files.…”
Section: Machine Learning For Pdf Malware Detectionmentioning
confidence: 99%
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“…As an alternative, machine learning is a popular approach for detecting spam, malware and network intrusion, and it can also be applied to classify PDF files [3,4] . The existing machine learning algorithms can use either static or dynamic features to train PDF classification models [5,6] . The difference is that static feature vectors can be directly obtained by processing a document, while dynamic feature vectors are obtained by monitoring the behavior of samples running in a built virtual environment.…”
Section: Introductionmentioning
confidence: 99%