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- Title
TLTD: A Testing Framework for Learning-Based IoT Traffic Detection Systems.
- Authors
Liu, Xiaolei; Zhang, Xiaosong; Guizani, Nadra; Lu, Jiazhong; Zhu, Qingxin; Du, Xiaojiang
- Abstract
With the popularization of IoT (Internet of Things) devices and the continuous development of machine learning algorithms, learning-based IoT malicious traffic detection technologies have gradually matured. However, learning-based IoT traffic detection models are usually very vulnerable to adversarial samples. There is a great need for an automated testing framework to help security analysts to detect errors in learning-based IoT traffic detection systems. At present, most methods for generating adversarial samples require training parameters of known models and are only applicable to image data. To address the challenge, we propose a testing framework for learning-based IoT traffic detection systems, TLTD. By introducing genetic algorithms and some technical improvements, TLTD can generate adversarial samples for IoT traffic detection systems and can perform a black-box test on the systems.
- Subjects
INTERNET of things; DEEP learning; VEHICLE detectors; TRAFFIC monitoring; PARAMETER estimation
- Publication
Sensors (14248220), 2018, Vol 18, Issue 8, p2630
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s18082630