Zhejiang Gongshang University

About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Adsorption & Supply chain. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.

Internet Traffic Classification Using Constrained Clustering

Abstract: Statistics-based Internet traffic classification using machine learning techniques has attracted extensive research interest lately, because of the increasing ineffectiveness of traditional port-based and payload-based approaches In particular, unsupervised learning, that is, traffic clustering, is very important in real-life applications, where labeled training data are difficult to obtain and new patterns keep emerging Although previous studies have applied some classic clustering algorithms such as K-Means and EM for the task, the quality of resultant traffic clusters was far from satisfactory In order to improve the accuracy of traffic clustering, we propose a constrained clustering scheme that makes decisions with consideration of some background information in addition to the observed traffic statistics Specifically, we make use of equivalence set constraints indicating that particular sets of flows are using the same application layer protocols, which can be efficiently inferred from packet headers according to the background knowledge of TCP/IP networking We model the observed data and constraints using Gaussian mixture density and adapt an approximate algorithm for the maximum likelihood estimation of model parameters Moreover, we study the effects of unsupervised feature discretization on traffic clustering by using a fundamental binning method A number of real-world Internet traffic traces have been used in our evaluation, and the results show that the proposed approach not only improves the quality of traffic clusters in terms of overall accuracy and per-class metrics, but also speeds up the convergence

Dynamic Mix-Zone for Location Privacy in Vehicular Networks

Abstract: Location privacy is an important issue in vehicular networks since knowledge of a vehicle's location can result in leakage of sensitive information. In this paper, we propose a method, named Dynamic Mix-zone for Location Privacy (DMLP). With DMLP, a vehicle' s mix-zone is dynamically formed at the time the vehicle requests it. The size of the mix-zone is determined by the vehicle's predicted location, the traffic statistics and the level of vehicle's privacy requirement. In order to provide unlinkability of new and old pseudonyms and locations, DMLP encrypts messages when transmitted while the vehicle is within the mix-zone. Simulation based analysis shows that DMLP offers a high level of location privacy and can be used regardless of the nature of the road at the location of the vehicle,e.g. highway, rural road or street in the downtown area of a city.