Abstract
Network traffic classification technologies could be used by attackers to implement network monitoring and then launch traffic analysis attacks or website fingerprint attacks. In order to prevent such attacks, a novel way to generate adversarial samples of network traffic from the perspective of the defender is proposed. By adding perturbation to the normal network traffic, a kind of adversarial network traffic is formed, which will cause misclassification when the attackers are implementing network traffic classification with deep convolutional neural networks (CNN) as a classification model. The paper uses the concept of adversarial samples in image recognition for reference to the field of network traffic classification and chooses several different methods to generate adversarial samples of network traffic. The experiment, in which the LeNet-5 CNN is selected as a classification model used by attackers and Vgg16 CNN is selected as the model to test the transferability of the adversarial network traffic generated, shows the effect of the adversarial network traffic samples.
Highlights
IntroductionAs a basic technology for enhancing network controllability, network traffic classification technology helps researchers understand traffic distribution, optimize network transmission, and improve network service quality; it is often leveraged by attackers for monitoring network traffic against the network targets and classifying the application types (such as mail, multimedia, and websites) the network traffic belong to
As a basic technology for enhancing network controllability, network traffic classification technology helps researchers understand traffic distribution, optimize network transmission, and improve network service quality; it is often leveraged by attackers for monitoring network traffic against the network targets and classifying the application types the network traffic belong to
The application of deep learning in network traffic classification can improve the accuracy of classification and has demonstrated huge potential in areas such as image recognition and natural language processing, adversaries against the deep learning models including the convolutional neural networks (CNN) have raised the interest of scholars on the concept of “Adversarial Sample” that was introduced to the area of computer vision by Szegedy et al [2]
Summary
As a basic technology for enhancing network controllability, network traffic classification technology helps researchers understand traffic distribution, optimize network transmission, and improve network service quality; it is often leveraged by attackers for monitoring network traffic against the network targets and classifying the application types (such as mail, multimedia, and websites) the network traffic belong to. The application of deep learning in network traffic classification can improve the accuracy of classification and has demonstrated huge potential in areas such as image recognition and natural language processing, adversaries against the deep learning models including the convolutional neural networks (CNN) have raised the interest of scholars on the concept of “Adversarial Sample” that was introduced to the area of computer vision by Szegedy et al [2]. In the study of image recognition, Szegedy has found that CNN tends to give an error output with high confidence degrees when intentionally adding some undetectable and tiny perturbations to the input samples of the learning models. On the contrary, the adversarial samples, from the perspective of defense, are of high value It can improve the robustness of deep learning models in responding to possible
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
