| Popis: |
The malicious software are still accounting up as a substantial threat to the cyber world. The most widely used vectors to infect different systems using malware are the document files. In this, the attacker tries to blend the malevolent code with the benign document files to carry out the attack. Portable document format (PDF) is the most commonly used document format to share the documents due to its portability and light weight. In this modern era, the attackers are implementing highly advance techniques to obfuscate the malware inside the document file. So, it becomes difficult for the malware detection classifiers to classify the document efficiently. These classifiers can be of two main type, namely, static and dynamic. In this paper, we surveyed various static and dynamic learning-based PDF malware classifiers to understand their architecture and working procedures. We also have presented the structure of the PDF files to understand the sections of PDF document where the malevolent code can be implanted. At the end, we performed a comparative study on the different surveyed classifiers by observing their true Positive percentages and F1 score. |
