基于多级分类的网络流量在线识别方法(英文)

Internet traffic classification plays an important role in network management. Many approaches have been proposed to classify different categories of Internet traffic. However, these approaches have specific usage contexts that restrict their ability when they are applied in the current network environment. For example, the port based approach cannot identify network applications with dynamic ports; the deep packet inspection approach is invalid for encrypted network applications; and the statistical based approach is time-consuming. In this paper, a novel technique is proposed to classify different categories of network applications. The port based, deep packet inspection based and statistical based approaches are integrated as a multistage classifier. The experimental results demonstrate that this approach has high recognition rate which is up to 98% and good performance of real-time for traffic identification.     

Method of data cleaning for network traffic classification

Network traffic classification aims at identifying the application types of network packets. It is important for Internet service providers （ISPs） to manage bandwidth resources and ensure the quality of service for different network applications However, most classification techniques using machine learning only focus on high flow accuracy and ignore byte accuracy. The classifier would obtain low classification performance for elephant flows as the imbalance between elephant flows and mice flows on Internet. The elephant flows, however, consume much more bandwidth than mice flows. When the classifier is deployed for traffic policing, the network management system cannot penalize elephant flows and avoid network congestion effectively. This article explores the factors related to low byte accuracy, and secondly, it presents a new traffic classification method to improve byte accuracy at the aid of data cleaning. Experiments are carried out on three groups of real-world traffic datasets, and the method is compared with existing work on the performance of improving byte accuracy. Experiment shows that byte accuracy increased by about 22.31% on average. The method outperforms the existing one in most cases.     

Advanced Leader Election for Virtual Traffic Lights

We examine the network performance of algorithms for self-organized traffic management. In particular, we focus on wireless network-ing between cars. One of many technologies that make road traffic safer and more efficient is the Virtual Traffic Light(VTL) system,which is able to coordinate the traffic flow at intersections without the need for physical lights. VTL takes a leading vehicle at an inter-section and uses it to control the traffic lights. We developed algorithms for leader election and traffic light computation in realistic ve-hicular networking scenarios. Our key contribution is the extension of this algorithm to support arbitrary intersection layouts. We in-vestigated the proposal in synthetic and realistic scenarios. The results show that, overall, VTLs use network resources efficiently andpositively influences driving experience. It performs better than stationary traffic lights for a low to medium network load. We alsoidentify potential optimizations to deal with high network load and to improve fairness.     

Congestion warning method based on the Internet of vehicles and community discovery of complex networks

The traffic congestion occurs frequently in urban areas, while most existing solutions only take effects after congesting. In this paper, a congestion warning method is proposed based on the Internet of vehicles(IOV) and community discovery of complex networks. The communities in complex network model of traffic flow reflect the local aggregation of vehicles in the traffic system, and it is used to predict the upcoming congestion. The real-time information of vehicles on the roads is obtained from the IOV, which includes the locations, speeds and orientations of vehicles. Then the vehicles are mapped into nodes of network, the links between nodes are determined by the correlations between vehicles in terms of location and speed. The complex network model of traffic flow is hereby established. The communities in this complex network are discovered by fast Newman(FN) algorithm, and the congestion warnings are generated according to the communities selected by scale and density. This method can detect the tendency of traffic aggregation and provide warnings before congestion occurs. The simulations show that the method proposed in this paper is effective and practicable, and makes it possible to take action before traffic congestion.     

Transport Layer Optimization for Cloud Computing Applications via Satellite： TCP Noordwijk＋

Cloud computing can significantly improve efficiency in Internet utilization and data management.Several cloud applications（file sharing,backup,data up/download etc.） imply transfers of large amount of data without real-time requirements.In several use-cases cloud-computing solutions reduce operational costs and guarantee target QoS.These solutions become critical when satellite systems are utilized,since resources are limited,network latency is huge and bandwidth costs are high.Using satellite capacity for cloud-computing bulk traffic,keeping acceptable performance of interactive applications,is very important and can limit the connectivity costs.This goal can be achieved installing in the Set Top Box（STB） a proxy agent,to differentiate traffic and assign bandwidth according to priority,leaving spare capacity to bulk cloud computing traffic.This aim is typically reached using a specific QoS architecture,adding functional blocks at network or lower layers.We propose to manage such a process at transport layer only.The endpoint proxy implements a new transport protocol called TCP Noordwijk＋,introducing a flow control differentiation capability.The proxy includes TPCN＋ which efficiently transfers low-priority bulk data and handles interactive data,keeping a high degree of friendliness.The outcomes of Ns-2simulations confirm applicability and good performance of the proposed solution.     

An Energy Balancing Adaptive Routing Strategy Based on Interference Model in 6LoWPANs

IPv6-enabled low-power wireless personal area network （6LoWPAN） is an important part of the Internet of Things which will drastically transform the way our society functions. Designing an efficient routing strategy is crucial for optimizing traffic over 6LoWPAN resources and extending the network lifetime. This paper proposes an energy balancing adaptive routing strategy named EBAR. Considering the dynamic traffic and constrained energy in 6LoWPAN, EBAR adaptively updates paths between different source-destination pairs and balances the energy in 6LoWPAN. Simulation results indicate that EBAR can promote network performance and balance the energy in the network.     

Trust Management Mechanism for Internet of Things

Trust management has been proven to be a useful technology for providing security service and as a consequence has been used in many applications such as P2P, Grid, ad hoc network and so on. However, few researches about trust mechanism for Internet of Things （IoT） could be found in the literature, though we argue that considerable necessity is held for applying trust mechanism to IoT. In this paper, we establish a formal trust management control mechanism based on architecture modeling of IoT. We decompose the IoT into three layers, which are sensor layer, core layer and application layer, from aspects of network composition of loT. Each layer is controlled by trust management for special purpose： self-organized, affective routing and multi-service respectively. And the final decision-making is performed by service requester according to the collected trust information as well as requester＇ policy. Finally, we use a formal semantics-based and fuzzy set theory to realize all above trust mechanism, the result of which provides a general framework for the development of trust models of IoT.     

Analysis on the time-domain characteristics of botnets control traffic

Botnets are networks composed with malware-infect ed computers.They are designed and organized to be controlled by an adversary.As victims are infected through their inappropriate network behaviors in most cases,the Internet protocol(IP) addresses of infected bots are unpredictable.Plus,a bot can get an IP address through dynamic host configuration protocol(DHCP),so they need to get in touch with the controller initiatively and they should attempt continuously because a controller can’t be always online.The whole process is carried out under the command and control(C&C) channel.Our goal is to characterize the network traffic under the C&C channel on the time domain.Our analysis draws upon massive data obtained from honeynet and a large Internet service provider(ISP) Network.We extract and summarize fingerprints of the bots collected in our honeynet.Next,with the fingerprints,we use deep packet inspection(DPI) Technology to search active bots and controllers in the Internet.Then,we gather and analyze flow records reported from network traffic monitoring equipments.In this paper,we propose a flow record interval analysis on the time domain characteristics of botnets control traffic,and we propose the algorithm to identify the communications in the C&C channel based on our analysis.After that,we evaluate our approach with a 3.4 GB flow record trace and the result is satisfactory.In addition,we believe that our work is also useful information in the design of botnet detection schemes with the deep flow inspection(DFI) technology.     

Offiine traffic analysis system based on Hadoop

Offiine network traffic analysis is very important for an in-depth study upon the understanding of network conditions and characteristics, such as user behavior and abnormal traffic. With the rapid growth of the amount of information on the Intemet, the traditional stand-alone analysis tools face great challenges in storage capacity and computing efficiency, but which is the advantages for Hadoop cluster. In this paper, we designed an offiine traffic analysis system based on Hadoop （OTASH）, and proposed a MapReduce-based algorithm for TopN user statistics. In addition, we studied the computing performance and failure tolerance in OTASH. From the experiments we drew the conclusion that OTASH is suitable for handling large amounts of flow data, and are competent to calculate in the case of single node failure.     

OpenFIow Based Flow Slice Load Balancing

Today＇s data center networks are designed using densely interconnected hosts in the data center.There are multiple paths between source host and destination server.Therefore,how to balance traffic is key issue with the fast growth of network applications.Although lots of load balancing methods have been proposed,the traditional approaches cannot fully satisfy the requirement of load balancing in data center networks.The main reason is the lack of efficient ways to obtain network traffic statistics from each network device.As a solution,the OpenFlow protocol enables monitoring traffic statistics by a centralized controller.However,existing solutions based on OpenFlow present a difficult dilemma between load balancing and packet reordering.To achieve a balance between load balancing and packet reordering,we propose an OpenFlow based flow slice load balancing algorithm.Through introducing the idea of differentiated service,the scheme classifies Internet flows into two categories：the aggressive and the normal,and applies different splitting granularities to the two classes of flows.This scheme improves the performance of load balancing and also reduces the number of reordering packets.Using the trace-driven simulations,we show that the proposed scheme gains over 50%improvement over previous schemes under the path delay estimation errors,and is a practical and efficient algorithm.     

THE ANALYSIS OF CELL LOSS RATES IN AN ATM MUX WITH LOSS PRIORITIES

The analysis of cell loss rates in an ATM Mux with loss priorities is an important problem in the-study of traffic control in ATM networks. In this paper, the loss rates of the cells with different priorities in an ATM Mux are analyzed by approximating the actual input process with two-state MMDP and fluid flow technique, and the analytical expressions of the relation between the loss rates and the buffer size are obtained. Simulation shows that the approach is sulliciently accurate for applications.     

Identifying file-sharing P2P traffic based on traffic characteristics

This article focuses on identifying file-sharing peer-to-peer （P2P） （such as BitTorrent （BT）） traffic at the borders of a stub network. By analyzing protocols and traffic of applications, it is found that file-sharing P2P traffic of a single user differs greatly from traditional and other P2P （such as QQ） applications＇ traffic in the distribution of involved remote hosts and remote ports. Therefore, a method based on discreteness of remote hosts （RHD） and discreteness of remote ports （RPD） is proposed to identify BT-like traffic. This method only relies on flow information of each user host in a stub network, and no packet payload needs to be monitored. At intervals, instant RHD for concurrent transmission control protocol and user datagram protocol flows for each host are calculated respectively through grouping flows by the stub network that the remote host of each flow belongs to. On given conditions, instant RPD are calculated through grouping flows by the remote port to amend instant RHD. Whether a host has been using a BT-like application or not can be deduced from instant RHD or average RHD for a period of time. The proposed method based on traffic characteristics is more suitable for identifying protean file-sharing P2P traffic than content-based methods Experimental results show that this method is effective with high accuracy.     

Inter-domain traffic balancing with co-channel interference management in multi-domain heterogeneous network for LTE-A

Heterogeneous network(Het-Net) is part of the long-term evolution advanced(LTE-A) study item and represents cellular deployments with a mixture of cells of different overlapping coverage areas,e.g.,a number of relay and pico cells overlaid by a macro cell in the same frequency.Traffic balancing and interference management are required in Het-Net design for LTE-A to maintain system performance.In this paper,we propose an inter-domain cooperative traffic balancing scheme focusing on reducing the effective resource cost and mitigating the co-channel interference in multi-domain Het-Net.We first set up the conception of multi-domain in Het-Net and incorporate the co-channel interference into the proposed traffic balancing scheme.Then we model the traffic balancing issue as a multi-domain traffic resource optimization problem for minimizing the effective resource cost.The detailed implementation for the proposed traffic balancing scheme is designed.In the numerical evaluation,the genetic algorithm(GA) as an optimization method is used to demonstrate that the total effective resource cost is significantly reduced through our proposed inter-domain traffic balancing scheme,comparing with the intra-domain traffic balancing scheme.The 43% of the resource cost is saved.In the system level simulation,the performance results of signal interference noise ratio(SINR) and throughput demonstrate that the proposed scheme has great advantages in interference management in Het-Net.     

Correcting Image Distortion for Adaptive Cruise Control

One of the main drivers for intelligent transportation systems is safety.Adaptive cruise control,as a common solution for traffic safety,has extended from radars to cameras.Due to high mobility of vehicles and unevenness of roads,the picture quality of cameras has been great challenges for camera-based adaptive cruise control.In this paper,an image distortion correction algorithm is addressed.Our method is based on optical flow technology which is normally applied in motion estimation and video compression research.We are the first to attempt to adapt it in image distortion correction.Two optical flow approaches,the Lucas-Kanade method and the Horn-Schunck method,are selected and compared.The procedure of image distortion correction using the optical flow method has been tested by both synthetic test images and camera images.The experimental results show that the Lucas-Kanade method is more suitable in the correction of image distortion.     

Key Technology and Experimental Research in Wireless Mesh Networks

With the increasing popularity and rising demand for high-rate wireless Internet access, traditional wireless access networks such as cellular network and Wireless Local Area Network (WLAN) are facing some challenges. The Wireless Mesh Network (WMN) is emerging as a flexible and low-cost alternative to provide multi-hop communications, supporting applications such as last-mile Internet delivery. The WMN has also become a promising technology in the merging of wireless networks. Some key technologies for the WMN networking, including network configuration, power control, mobility management, access control, and routing protocols, are analyzed; the routing protocol design in WMN and mobile Ad hoc network are compared. An example of WMN testbed based on WLAN and second-layer switching technology is given.     

Resource allocation and hybrid prediction scheme for low-latencyvisual feedbacks to support tactile Internet multimodal perceptions

Predicting user states in future and rendering visual feedbacks accordingly can effectively reduce the visual experienced delay in the tactile Internet (TI). However, most works omit the fact that different parts in an image may have distinct prediction requirements, based on which different prediction models can be used in the predicting process, and then it can further improve predicting quality especially under resources-limited environment. In this paper, a hybrid prediction scheme is proposed for the visual feedbacks in a typical TI scenario with mixed visuo-haptic interactions, in which haptic traffic needs sufficient wireless resources to meet its stringent communication requirement, leaving less radio resources for the visual feedback. First, the minimum required number of radio resources for haptic traffic is derived based on the haptic communication requirements, and wireless resources are allocated to the haptic and visual traffics afterwards. Then, a grouping strategy is designed based on the deep neural network (DNN) to allocate different parts from an image feedback into two groups to use different prediction models, which jointly considers the prediction deviation thresholds, latency and reliability requirements, and the bit sizes of different image parts. Simulations show that, the hybrid prediction scheme can further reduce the visual experienced delay under haptic traffic requirements compared with existing strategies.     

Chaotic neural network for associative memory

Based on current research on applications of chaotic neuron network for information processing, the stability and convergence of chaotic neuron network are proved from the viewpoint of energy function. Moreover, a new auto-associative matrix is devised for artificial neural network composed of chaotic neurons, thus, an improved chaotic neuron network for associative memory is built up. Finally, the associative recalling process of the network is analyzed in detail and explanations of improvement are given.     

MILITARY MULTI-HOP ROUTING PROTOCOL

Military tactical network is one of the most important applications of ad hoc network. Currently the existing routing protocols are put forward based on the simple and universal network model which has the ability of multi-hop routing but cannot work efficiently in the special military applications. The narrow bandwidth and limited energy of the wireless network make general routing protocols seem to be more redundant. In order to satisfy military needs properly, this letter makes a detailed analysis of physical nodes and traffic distribution in military network, uses cross-layer design concept for reference, combines traffic characteristic of application layer with routing protocol design. It categorized routing problem as crucial routes and non-crucial routes which adopted separate maintenance methods. It not only realizes basic transmission function but also works efficiently. It is quite fit for military application.     

Virtual channel load-balanced routing algorithm for Torus networks

In order to improve the network performance furthermore,a routing algorithm for 2D-Torus is investigated from the standpoint of load balance for virtual channels.The 2D-Torus network is divided into two virtual networks and each physical channel is split into three virtual channels.A novel virtual channel allocation policy and a routing algorithm are proposed,in which traffic load is distributed to those three virtual channels in a more load-balanced manner by introducing a random parameter.Simulations of the proposed algorithm are developed with a SystemC-based test bench.The results show that compared with the negative first for Torus networks(NF-T) algorithm,the proposed algorithm can achieve better performance in terms of network latency and throughput under different traffic patterns.It also shows that a routing algorithm with load balance for virtual channels can significantly improve the network performance furthermore.     

Unveiling the Challenges in Improving Data Availability in Vehicular Networks with Network Coding

Retrieving data from mobile source vehicles is a crucial routine operation for a wide spectrum of vehicular network applications, in-cluding road surface monitoring and sharing. Network coding has been widely exploited and is an effective technique for diffusing in-formation over a network. The use of network coding to improve data availability in vehicular networks is explored in this paper. Withrandom linear network codes, simple replication is avoided, and instead, a node forwards a coded block that is a random combinationof all data received by the node. We use a network-coding-based approach to improve data availability in vehicular networks. To deter-mine the feasibility of this approach, we conducted an empirical study with extensive simulations based on two real vehicular GPStraces, both of which contain records from thousands of vehicles over more than a year. We observed that, despite significant improve-ment in data availability, there is a serious issue with linear correlation between the received codes. This reduces the data-retrievalsuccess rate. By analyzing the real vehicular traces, we discovered that there is a strong community structure within a real vehicularnetwork. We verify that such a structure contributes to the issue of linear dependence. Then, we point out opportunities to improve thenetwork-coding-based approach by developing community-aware code-distribution techniques.