TLTD: A testing framework for learning-based IoT traffic detection systems

Xiaolei Liu, Xiaosong Zhang, Nadra Guizani, Jiazhong Lu, Qingxin Zhu, Xiaojiang Du

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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.

Original languageEnglish
Article number2630
JournalSensors (Switzerland)
Volume18
Issue number8
DOIs
StatePublished - 10 Aug 2018

Keywords

  • Adversarial samples
  • Internet of things
  • Machine learning
  • Traffic detection

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