School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Cyberspace Security Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel). 2018 Aug 10;18(8):2630. doi: 10.3390/s18082630.
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.
随着物联网（IoT）设备的普及和机器学习算法的不断发展，基于学习的物联网恶意流量检测技术逐渐成熟。然而，基于学习的物联网流量检测模型通常非常容易受到对抗样本的攻击。因此，非常需要一个自动化的测试框架来帮助安全分析师检测基于学习的物联网流量检测系统中的错误。目前，大多数生成对抗样本的方法都需要已知模型的训练参数，并且仅适用于图像数据。为了解决这个挑战，我们提出了一个用于学习的物联网流量检测系统的测试框架，TLTD。通过引入遗传算法和一些技术改进，TLTD 可以为物联网流量检测系统生成对抗样本，并对系统进行黑盒测试。
