Conference proceeding
Lightweight Behavioral Malware Detection for Windows Platforms
PROCEEDINGS OF THE 2017 12TH INTERNATIONAL CONFERENCE ON MALICIOUS AND UNWANTED SOFTWARE (MALWARE), pp 75-81
01 Jan 2017
Abstract
We describe a lightweight behavioral malware detection technique that leverages Microsoft Windows prefetch files. We demonstrate that our malware detection achieves a high detection rate with a low false-positive rate of 1 x 10(-3), and scales linearly for training samples. We demonstrate the generalization of our malware detection on two different Windows platforms with a different set of applications. We study the loss in performance of our malware detection in case of concept drift and its ability to adapt. Finally, we measure our malware detection against evasive malware and present an effective auxiliary defensive technique against such attacks.
Metrics
Details
- Title
- Lightweight Behavioral Malware Detection for Windows Platforms
- Creators
- Bander Alsulami - Drexel Univ, Philadelphia, PA 19104 USAAvinash Srinivasan - Temple Univ, Philadelphia, PA 19122 USAHunter Dong - Temple Univ, Philadelphia, PA 19122 USASpiros Mancoridis - Drexel University, Computer Science
- Publication Details
- PROCEEDINGS OF THE 2017 12TH INTERNATIONAL CONFERENCE ON MALICIOUS AND UNWANTED SOFTWARE (MALWARE), pp 75-81
- Publisher
- IEEE
- Number of pages
- 7
- Grant note
- Isaac L. Auerbach Cybersecurity Institute at Drexel University
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000445239300009
- Scopus ID
- 2-s2.0-85050885824
- Other Identifier
- 991019170320704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Software Engineering
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic