Citation

Liu X, Zhang X, Guizani N, Lu J, Zhu Q, Du X. Sensors (Basel) 2018; 18(8): s18082630.

Affiliation

Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA. dux@temple.edu.

Copyright

(Copyright © 2018, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s18082630

PMID

30103460

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.

Language: en

Keywords

adversarial samples; internet of things; machine learning; traffic detection