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Malware detection using system logs

Nguyen, Nhu T., Pham, Thuy T., Dang, Tien X., Dao, Minh-Son, Dang-Nguyen, Duc-Tien orcid logoORCID: 0000-0002-2761-2213, Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 and Nguyen, Binh T. (2020) Malware detection using system logs. In: 2020 Intelligent Cross-Data Analysis and Retrieval Workshop (ICDAR'20), 26 Oct 2020, Dublin, Ireland. ISBN 978-1-4503-7087-5

Abstract
Malware detection is one of the most critical features in many real applications, especially for the mobile platform and the Internet of Things (IoT) technology. Due to the proliferation of mobile devices and the associated app-stores, the volume of new applications growing extremely fast requires a better way to analyze all possible malicious behaviors. In this paper, we investigate the malware prediction problem using system log files that contain numbers of sequences of system calls recorded from IoT devices. We construct a suitable multi-class classification model by using the combination of hand-crafted features, (including Bag-of-Ngrams, TF-IDF, and the statistical metrics computed from the consecutive repeated system calls in each log file). Also, we consider different machine learning models, including Random Forest, Support Vector Machines, and Extreme Gradient Boosting, and measure the performance of each method in terms of precision, recall, and F1-score. The experimental results show that a combination of different features, as well as using the Extreme Gradient Boosting technique, can help us to achieve promising performance in the dataset provided by the organizers of the competition CMDC 2019.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Uncontrolled Keywords:malware detection; SVMs; IoT; XGBoost; random forest
Subjects:Computer Science > Computer security
Computer Science > Software engineering
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 2020 Intelligent Cross-Data Analysis and Retrieval Workshop (ICDAR ’20). . Association for Computing Machinery (ACM). ISBN 978-1-4503-7087-5
Publisher:Association for Computing Machinery (ACM)
Official URL:https://doi.org/10.1145/3379174.3392318
Copyright Information:© 2020 ACM
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland under grant number SFI/13/RC/2106, L. Meltzers Høyskolefonds, UiB 2019/2259-NILSO
ID Code:24668
Deposited On:22 Jun 2020 15:47 by Cathal Gurrin . Last Modified 15 Dec 2021 15:40
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