TY - JOUR
T1 - TLTD
T2 - A testing framework for learning-based IoT traffic detection systems
AU - Liu, Xiaolei
AU - Zhang, Xiaosong
AU - Guizani, Nadra
AU - Lu, Jiazhong
AU - Zhu, Qingxin
AU - Du, Xiaojiang
N1 - Publisher Copyright:
© 2018 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2018/8/10
Y1 - 2018/8/10
N2 - 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.
AB - 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.
KW - Adversarial samples
KW - Internet of things
KW - Machine learning
KW - Traffic detection
UR - http://www.scopus.com/inward/record.url?scp=85051412392&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051412392&partnerID=8YFLogxK
U2 - 10.3390/s18082630
DO - 10.3390/s18082630
M3 - Article
C2 - 30103460
AN - SCOPUS:85051412392
SN - 1424-8220
VL - 18
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 8
M1 - 2630
ER -