Conference proceeding
IoT Malware Data Augmentation using a Generative Adversarial Network
Proceedings of the 57th Annual Hawaii International Conference on System Sciences, pp 7572-7581
01 Jan 2024
Abstract
Behavioral malware detection has been shown to be an effective method for detecting malware running on computing hosts. Machine learning (ML) models are often used for this task, which use representative behavioral data from a device to make a classification as to whether an observation is malware or not. Although these models can perform well, machine learning models in security are often trained on imbalanced training datasets that yield poor real-world efficacy, as they favor the overrepresented class. Thus, we need a way to augment the underrepresented class. Some common data augmentation techniques include SMOTE, data resampling/upsampling, or using generative algorithms. In this work, we explore using generative algorithms for this task, and show how those results compare to results obtained using SMOTE and upsampling. Specifically, we feed the less-represented class of data into a Generative Adversarial Network (GAN) to create enough realistic synthetic data to balance the dataset. In this work, we show how using a GAN to balance a dataset that favors benign data helps a shallow Neural Network achieve a higher Area Under the Receiver Operating Characteristic Curve (AUC) and a lower False Positive Rate (FPR).
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Details
- Title
- IoT Malware Data Augmentation using a Generative Adversarial Network
- Creators
- John Carter - Drexel UniversitySpiros Mancoridis - Drexel UniversityPavlos Protopapas - Harvard UniversityErick Galinkin - Drexel University
- Contributors
- T X Bui (Editor)
- Publication Details
- Proceedings of the 57th Annual Hawaii International Conference on System Sciences, pp 7572-7581
- Conference
- Hawaii International Conference on System Sciences, 57 (Waikiki, USA, 03 Jan 2024–06 Jan 2024)
- Series
- Hawaii International Conference on System Sciences
- Publisher
- HICSS
- Number of pages
- 10
- Grant note
- Auerbach Berger Chair of Cybersecurity
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:001301787507066
- Other Identifier
- 991022040296604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications
- Computer Science, Software Engineering