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Sensors 2018, 18(8), 2630; https://doi.org/10.3390/s18082630

TLTD: A Testing Framework for Learning-Based IoT Traffic Detection Systems

1
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
2
Cyberspace Security Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
3
Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA
4
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
*
Author to whom correspondence should be addressed.
Received: 27 June 2018 / Revised: 3 August 2018 / Accepted: 3 August 2018 / Published: 10 August 2018
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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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. View Full-Text
Keywords: internet of things; traffic detection; adversarial samples; machine learning internet of things; traffic detection; adversarial samples; machine learning
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Liu, X.; Zhang, X.; Guizani, N.; Lu, J.; Zhu, Q.; Du, X. TLTD: A Testing Framework for Learning-Based IoT Traffic Detection Systems. Sensors 2018, 18, 2630.

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