Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic markedly.Over the past few decades,network traffic identification has been a research hotspot in the field of network management and security mon-itoring.However,as more network services use encryption technology,network traffic identification faces many challenges.Although classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and labor-intensive.Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples.However,in real scenarios,labeled samples are often difficult to obtain.This paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning.Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

A CPK-Based Identity Authentication Scheme for IoT

As the power Internet of Things(IoT)enters the security construction stage,the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and practicality.The existing public key infrastructure(PKI)-based security authentication scheme is currently difficult to apply in many terminals in IoT.Its key distribution and management costs are high,which hinders the development of power IoT security construction.Combined Public Key(CPK)technology uses a small number of seeds to generate unlimited public keys.It is very suitable for identity authentication in the power Internet of Things.In this paper,we propose a novel identity authentication scheme for power IoT.The scheme combines the physical unclonable function(PUF)with improved CPK technology to achieve mutual identity authentication between power IoT terminals and servers.The proposed scheme does not require third-party authentication and improves the security of identity authentication for power IoT.Moreover,the scheme reduces the resource consumption of power IoT devices.The improved CPK algorithm solves the key collision problem,and the third party only needs to save the private key and the public key matrix.Experimental results show that the amount of storage resources occupied in our scheme is small.The proposed scheme is more suitable for the power IoT.