ISBN 978-3-8439-3409-1

978-3-8439-3409-1, Reihe Informatik

Nedim Šrndić
Machine Learning and Security of Non-Executable Files

162 Seiten, Dissertation Eberhard-Karls-Universität Tübingen (2017), Hardcover, A5

Zusammenfassung / Abstract

Computer malware is a well-known threat in security which, despite the enormous time and effort invested in fighting it, is today more prevalent than ever. Recent years have brought a surge in one particular type: malware embedded in non-executable file formats, e.g., PDF, SWF and various office file formats. The result have been regular discoveries of new vulnerabilities.

The traditional approach to malware detection - signature matching, heuristics and behavioral profiling - has from its inception been a labor-intensive manual task, always a step behind the attacker. With the exponential growth of computers and networks, malware has become more diverse, wide-spread and adaptive than ever. An automated and scalable approach is needed to fill the gap between automated malware adaptation and manual malware detection, and machine learning is emerging as a viable solution. Its branch called adversarial machine learning studies the security of machine learning algorithms and the special conditions that arise when machine learning is applied for security.

This thesis is a study of adversarial machine learning in the context of static detection of malware in non-executable file formats. It evaluates the effectiveness, efficiency and security of machine learning applications in this context. To this end, it introduces 3 data-driven detection methods developed using large, high quality datasets. PJScan detects malicious PDF files based on lexical properties of embedded JavaScript code and is the fastest method published to date. SL2013 extends its coverage to all PDF files, regardless of JavaScript presence, by analyzing the hierarchical structure of PDF logical building blocks and demonstrates excellent performance in a novel long-term realistic experiment. Finally, Hidost generalizes the hierarchical-structure-based feature set to become the first machine-learning-based malware detector operating on multiple file formats. In a comprehensive evaluation on PDF and SWF, it outperforms other academic methods and commercial antivirus systems.

Furthermore, the thesis presents a framework for security evaluation of machine learning classifiers in a case study performed on an independent PDF malware detector. The results show that the ability to manipulate a part of the classifier's feature set allows a malicious adversary to disguise malware so that it appears benign to the classifier. The presented methods are released as open-source software.