Bottleneck Analysis for Data Acquisition in High-Speed Network Traffic Monitoring

The increasing network throughput challenges the current network traffic monitor systems to have compatible high-performance data processing.The design of packet processing systems is guided by the requirements of high packet processing throughput.In this paper,we depict an in-depth research on the related techniques and an implementation of a high-performance data acquisition mechanism.Through the bottleneck analysis with the aid of queuing network model,several performance optimising methods,such as service rate increasing,queue removing and model simplification,are integrated.The experiment results indicate that this approach is capable of reducing the CPU utilization ratio while improving the efficiency of data acquisition in high-speed networks.

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.

Threshold Ring Signature Scheme Based on TPM

The conventional ring signature schemes cannot address the scenario where the rank of members of the ring needs to be distinguished, for example, in electronically commerce application. To solve this problem, we presented a Trusted Platform Module (TPM)-based threshold ring signature scheme. Employing a reliable secret Share Distribution Center (SDC), the proposed approach can authenticate the TPM-based identity rank of members of the ring but not track a specific member's identity. A subset including t members with the same identity rank is built. With the signing cooperation of t members of the subset, the ring signature based on Chinese remainder theorem is generated. We proved the anonymity and unforgeability of the proposed scheme and compared it with the threshold ring signature based on Lagrange interpolation polynomial. Our scheme is relatively simpler to calculate.

Data Security and Privacy in Bitcoin System:A Survey

To date,bitcoin has been the most successful application of blockchain technology and has received considerable attention from both industry and academia.Bitcoin is an electronic payment system based on cryptography rather than on credit.Regardless of whether people are in the same city or country,bitcoin can be sent by any one person to any other person when they reach an agreement.The market value of bitcoin has been rising since its advent in 2009,and its current market value is US160 billion.Since its development,bitcoin itself has exposed many problems and is facing challenges from all the sectors of society;therefore,adversaries may use bitcoin's weakness to make considerable profits.This survey presents an overview and detailed investigation of data security and privacy in bitcoin system.We examine the studies in the literature/Web in two categories:1)analyses of the attacks to the privacy,availability,and consistency of bitcoin data and 2)summaries of the countermeasures for bitcoin data security.Based on the literature/Web,we list and describe the research methods and results for the two categories.We compare the performance of these methods and illustrate the relationship between the performance and the methods.Moreover,we present several important open research directions to identify the follow-up studies in this area.