Publication Date:
2018-08-11
Description:
Sensors, Vol. 18, Pages 2630: TLTD: A Testing Framework for Learning-Based IoT Traffic Detection Systems Sensors doi: 10.3390/s18082630 Authors: Xiaolei Liu Xiaosong Zhang Nadra Guizani Jiazhong Lu Qingxin Zhu Xiaojiang Du 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.
Electronic ISSN:
1424-8220
Topics:
Chemistry and Pharmacology
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Electrical Engineering, Measurement and Control Technology
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