Showing papers on "Network traffic simulation published in 2009"

Traffic Morphing: An Efficient Defense Against Statistical Traffic Analysis.

Abstract: Recent work has shown that properties of network traffic that remain observable after encryption, namely packet sizes and timing, can reveal surprising information about the traffic’s contents (e.g., the language of a VoIP call [29], passwords in secure shell logins [20], or even web browsing habits [21, 14]). While there are some legitimate uses for encrypted traffic analysis, these techniques also raise important questions about the privacy of encrypted communications. A common tactic for mitigating such threats is to pad packets to uniform sizes or to send packets at fixed timing intervals; however, this approach is often inefficient. In this paper, we propose a novel method for thwarting statistical traffic analysis algorithms by optimally morphing one class of traffic to look like another class. Through the use of convex optimization techniques, we show how to optimally modify packets in real-time to reduce the accuracy of a variety of traffic classifiers while incurring much less overhead than padding. Our evaluation of this technique against two published traffic classifiers for VoIP [29] and web traffic [14] shows that morphing works well on a wide range of network data—in some cases, simultaneously providing better privacy and lower overhead than naive

RAINBOW: A Robust And Invisible Non-Blind Watermark for Network Flows.

Abstract: Linking network flows is an important problem in intrusion detection as well as anonymity Passive traffic analysis can link flows but requires long periods of observation to reduce errors Watermarking techniques allow for better precision and blind detection, but they do so by introducing significant delays to the traffic flow, enabling attacks that detect and remove the mark, while at the same time slowing down legitimate traffic We propose a new, non-blind watermarking scheme called RAINBOW that is able to use delays hundreds of times smaller than existing watermarks by eliminating the interference caused by the flow in the blind case As a result, our watermark is invisible to detection, as confirmed by experiments using information-theoretic detection tools We analyze the error rates of our scheme based on a mathematical model of network traffic and jitter We also validate the analysis using an implementation running on PlanetLab We find that our scheme generates orders of magnitudes lower rates of false errors than passive traffic analysis, while using only a few hundred observed packets We also extend our scheme so that it is robust to packet drops and repacketization and show that flows can still be reliably linked, though at the cost of somewhat longer observation periods

Stochastic multi-objective production-distribution network design using simulation-based optimization

Abstract: This paper addresses the design of production-distribution networks including both supply chain configuration and related operational decisions such as order splitting, transportation allocation and inventory control. The goal is to achieve the best compromise between cost and customer service level. An optimization methodology that combines a multi-objective genetic algorithm (MOGA) and simulation is proposed to optimize not only the structure of the production-distribution network but also its operation strategies and related control parameters. A flexible simulation framework is developed to enable the automatic simulation of the production-distribution network with all possible configurations and all possible control strategies. To illustrate its effectiveness, the proposed method is applied to a real life case study from automotive industry.

Swing: realistic and responsive network traffic generation

Abstract: This paper presents Swing, a closed-loop, network-responsive traffic generator that accurately captures the packet interactions of a range of applications using a simple structural model. Starting from observed traffic at a single point in the network, Swing automatically extracts distributions for user, application, and network behavior. It then generates live traffic corresponding to the underlying models in a network emulation environment running commodity network protocol stacks. We find that the generated traffic is statistically similar to the original traffic. Furthermore, to the best of our knowledge, we are the first to reproduce burstiness in traffic across a range of time-scales using a model applicable to a variety of network settings. An initial sensitivity analysis reveals the importance of our individual model parameters to accurately reproduce such burstiness. Finally, we explore Swing's ability to vary user characteristics, application properties, and wide-area network conditions to project traffic characteristics into alternate scenarios.

A review of traffic simulation software

Abstract: Computer simulation of traffic is a widely used method in research of traffic modelling, planning and development of traffic networks and systems. Vehicular traffic systems are of growing concern and interest globally and modelling arbitrarily complex traffic systems is a hard problem. In this article we review some of the traffic simulation software applications, their features and characteristics as well as the issues these applications face. Additionally, we introduce some algorithmic ideas, underpinning data structural approaches and quantifiable metrics that can be applied to simulated model systems.

Intervention and Causality: Forecasting Traffic Flows Using a Dynamic Bayesian Network

Abstract: Real-time traffic flow data across entire networks can be used in a traffic management system to monitor current traffic flows so that traffic can be directed and managed efficiently. Reliable short-term forecasting models of traffic flows are crucial for the success of any traffic management system. The model proposed in this article for forecasting traffic flows is a multivariate Bayesian dynamic model called the multiregression dynamic model (MDM). This model is an example of a dynamic Bayesian network and is designed to preserve the conditional independences and causal drive exhibited by the traffic flow series. Sudden changes can occur in traffic flow series in response to such events as traffic accidents or roadworks. A traffic management system is particularly useful at such times of change. To ensure that the associated forecasting model continues to produce reliable forecasts, despite the change, the MDM uses the technique of external intervention. This article will demonstrate how intervention w...

Traffic Engineering vs. Content Distribution: A Game Theoretic Perspective

Abstract: In this paper we explore the interaction between content distribution and traffic engineering. Because a traffic engineer may be unaware of the structure of content distribution systems or overlay networks, this management of the network does not fully anticipate how traffic might change as a result of his actions. Content distribution systems that assign servers at the application level can respond very rapidly to changes in the routing of the network. Consequently, the traffic engineer's decisions may almost never be applied to the intended traffic. We use a game-theoretic framework in which infinitesimal users of a network select the source of content, and the traffic engineer decides how the traffic will route through the network. We formulate a game and prove the existence of equilibria. Additionally, we present a setting in which equilibria are socially optimal, essentially unique, and stable. Conditions under which efficiency loss may be bounded are presented, and the results are extended to the cases of general overlay networks and multiple autonomous systems.

Exploiting dynamicity in graph-based traffic analysis: techniques and applications

Abstract: Network traffic can be represented by a Traffic Dispersion Graph (TDG) that contains an edge between two nodes that send a particular type of traffic (e.g., DNS) to one another. TDGs have recently been proposed as an alternative way to interpret and visualize network traffic. Previous studies have focused on static properties of TDGs using graph snapshots in isolation. In this work, we represent network traffic with a series of related graph instances that change over time. This representation facilitates the analysis of the dynamic nature of network traffic, providing additional descriptive power. For example, DNS and P2P graph instances can appear similar when compared in isolation, but the way the DNS and P2P TDGs change over time differs significantly. To quantify the changes over time, we introduce a series of novel metrics that capture changes both in the graph structure (e.g., the average degree) and the participants (i.e., IP addresses) of a TDG. We apply our new methodologies to improve graph-based traffic classification and to detect changes in the profile of legacy applications (e.g., e-mail).

A General Neural Network Model for Estimating Telecommunications Network Reliability

Abstract: This paper puts forth a new encoding method for using neural network models to estimate the reliability of telecommunications networks with identical link reliabilities. Neural estimation is computationally speedy, and can be used during network design optimization by an iterative algorithm such as tabu search, or simulated annealing. Two significant drawbacks of previous approaches to using neural networks to model system reliability are the long vector length of the inputs required to represent the network link architecture, and the specificity of the neural network model to a certain system size. Our encoding method overcomes both of these drawbacks with a compact, general set of inputs that adequately describe the likely network reliability. We computationally demonstrate both the precision of the neural network estimate of reliability, and the ability of the neural network model to generalize to a variety of network sizes, including application to three actual large scale communications networks.

Systems and Methods for A Simulated Network Environment and Operation Thereof

Abstract: Methods and Systems for simulating a network environment includes simulating network components and network architecture of a user network, simulating real-world network traffic on the simulated user network, simulating network events within the simulated real-world traffic on the simulated user network, monitoring the simulated network, the simulated traffic including the simulated network events, and receiving input from a user, such inputs manually controlling/managing the simulated network components of the simulated user network responsive to the monitoring.

Traffic Shaping for Resource-Efficient In-Vehicle Communication

Abstract: In-vehicle communication has become complex and costly due to the growing number of automotive network systems applied for different data types. In this work, our previously proposed in-vehicle network architecture that is based on Internet protocol (IP) and full-duplex switched Ethernet (IP/Ethernet) is further investigated for real-time audio and video streaming. Quality-of-service (QoS) and resource usage are analyzed for selected IP/Ethernet-based network topologies. Traffic shaping is used to reduce the required network resources and consequently the cost. A novel traffic shaping algorithm is presented that outperforms other traffic shapers in terms of resource usage when applied to variable bit rate video sources in the proposed double star topology. In addition, a new architecture design is introduced for traffic shaper implementation in switches which operates on a per stream basis. Analytical and simulation results confirm that the proposed network architecture with traffic shaping is well-adapted for in-vehicle communication.

A comparative study of network link emulators

Abstract: Between discrete event simulation and evaluation within real networks, network emulation is a useful tool to study and evaluate the behaviour of applications. Using a real network as a basis to simulate another network's characteristics, it enables researchers to perform experiments in a wide range of conditions. After an overview of the various available network emulators, this paper focuses on three freely available and widely used network link emulators: Dummynet, NIST-Net, and the Linux Traffic Control subsystem. We start by comparing their features, then focus on the accuracy of their latency and bandwidth emulation, and discuss the way they are affected by the time source of the system. We expose several problems that cannot be ignored when using such tools. We also outline differences in their user interfaces, such as the interception point, and discuss possible solutions. This work aims at providing a complete overview of the different solutions for network emulation.

Optimal budget allocation for discrete-event simulation experiments

Abstract: Simulation plays a vital role in analyzing discrete-event systems, particularly in comparing alternative system designs with a view to optimizing system performance. Using simulation to analyze complex systems, however, can be both prohibitively expensive and time-consuming. Effective algorithms to allocate intelligently a computing budget for discrete-event simulation experiments are presented in this paper. These algorithms dynamically determine the simulation lengths for all simulation experiments and thus significantly improve simulation efficiency under the constraint of a given computing budget. Numerical illustrations are provided and the algorithms are compared with traditional two-stage ranking-and-selection procedures through numerical experiments. Although the proposed approach is based on heuristics, the numerical results indicate that it is much more efficient than the compared procedures.

Network routing method and system utilizing label-switching traffic engineering queues

Abstract: The present invention is directed to a scalable packet-switched network routing method and system that utilizes modified traffic engineering mechanisms to prioritize tunnel traffic and non-tunnel traffic. The method includes the steps of receiving a request to establish a traffic engineering tunnel across the packet-switched network. Then at a router traversed by the traffic engineering tunnel, a queue for packets carried inside the traffic engineering tunnel is created. Subsequently, bandwidth for the queue is reserved in accordance with the request to establish the traffic engineering tunnel, wherein the queue created for packets carried inside the traffic the traffic engineering tunnel is given priority over other traffic at the router and the reserved bandwidth for the queue can only be used by packets carried inside the traffic engineering tunnel.

Stochastic Geometry and Telecommunications Networks

Abstract: Just as queueing theory revolutionized the study of circuit switched telephony in the twentieth century, stochastic geometry is gradually becoming a necessary theoretical tool for modelling and analysis of modern telecommunications systems, in which spatial arrangement is typically a crucial consideration in their performance evaluation, optimization or future development. In this survey we aim to summarize the main stochastic geometry models and tools currently used in studying modern telecommunications. We outline specifics of wired, wireless fixed and ad hoc systems and show how stochastic geometry modelling helps in their analysis and optimization. Point and line processes, Palm theory, shot-noise processes, random tessellations, Boolean models, percolation, random graphs and networks, spatial statistics and optimization: this is a far from exhaustive list of techniques used in studying contemporary telecommunications systems and which we shall briefly discuss.

A Novel BP Neural Network Model for Traffic Prediction of Next Generation Network

Abstract: Network traffic prediction is an important research aspect of network behavior. Conventionally, ARMA time sequence model is usually adopted in network traffic prediction. However, the parameters used in normal time sequence models are difficult to be estimated and the nonstationary time sequence problem can not be processed using ARMA time sequence model. The neural network techniques may memory large quantity of characteristics of data set by learning previous data, and is suitable for solving these problems with large complexity. IP6 network traffic prediction is just the problem with nonlinear feature and can be solved using appropriate neural network model. In this paper, according to the daily cycle characteristic of IPv6 network traffic, a novel transfer function is designed, which has lots of advantages such as fast convergence and high precision. Based on the new transfer function, an improved BP neural network model is produced, and a IPv6 network traffic prediction system is implemented. Using this new BP neural network model to process the actual data, the results present that our model has a faster learning ability and has a higher precision compared with previous BP neural network model. Therefore, this BP neural network model can be used for normal traffic prediction in current IPv6 network.

Mathematical modelling of network traffic flow

Abstract: The article describes mathematical models of traffic flows to initiate different traffic flow processes. Separate elements of traffic flow models are made in a way to be connected together to get a single complex model. A model of straight road with different boundary conditions is presented as a separate part of the network traffic flow model. First testing is conducted in case the final point of the whole modelled traffic line is closed and no output from that point is possible. The second test is performed when a constant value of traffic flow speed and traffic flow rate is entered. Mathematical simulation is carried out and the obtained results are listed.

Method and system for traffic simulation of road network

Abstract: According to one embodiment, a system is disclosed, which uses road parameters defining the road network and model parameters used as initial-value parameters, thereby performing traffic simulation by the microsimulation method. The system includes a traffic simulator and a display controller. The traffic simulator performs traffic simulation to predict a traffic condition on an object road of a road network. The display controller controls a display unit, displaying the result of the simulation. More precisely, the display controller displays a dynamic image showing the traffic condition of vehicles running on the road network, on the screen of the display unit, and changes the image in terms of pattern, in accordance with a display instruction.

A Time Series Modeling and prediction of wireless Network Traffic

Abstract: The number of users and their network utilization will enumerate the traffic of the network. The accurate and timely estimation of network traffic is increasingly becoming important in achieving guaranteed Quality of Service (QoS) in a wireless network. The better QoS can be maintained in the network by admission control, inter or intra network handovers by knowing the network traffic in advance. Here wireless network traffic is modeled as a nonlinear and nonstationary time series. In this framework, network traffic is predicted using neural network and statistical methods. The results of both the methods are compared on different time scales or time granularity. The Neural Network(NN) architectures used in this study are Recurrent Radial Basis Function Network (RRBFN) and Echo state network (ESN).The statistical model used here in this work is Fractional Auto Regressive Integrated Moving Average (FARIMA) model. The traffic prediction accuracy of neural network and statistical models are in the range of 96.4% to 98.3% and 78.5% to 80.2% respectively.

Network real time monitoring and control system

Abstract: A network real-time monitoring and control system includes several layers of components for generating a network traffic shaping control that is used to shaping network traffic flows for one or more network nodes. The layers of the network real-time monitoring and control system include a monitoring layer, an event control layer, a traffic shaping control layer, a reporting layer, and an administrative layer. The monitoring obtains network traffic indicator measurements and network node operational indicator measurements. The event control layer uses the indicator measurements to generate a network event identifier, and generates a request for a network traffic shaping control based on a correlation of the network event identifier with a network node status identifier. The request for the network traffic shaping control is communicated to the traffic shaping control layer to generate a network traffic shaping control.

HebbR2-Taffic: A novel application of neuro-fuzzy network for visual based traffic monitoring system

Abstract: This paper presents a robust methodology that automatically counts moving vehicles along an expressway. The domain of interest for this paper is using both neuro-fuzzy network and simple image processing techniques to implement traffic flow monitoring and analysis. As this system is dedicated for outdoor applications, efficient and robust processing methods are introduced to handle both day and night analysis. In our study, a neuro-fuzzy network based on the Hebbian-Mamdani rule reduction architecture is used to classify and count the number of vehicles that passed through a three- or four-lanes expressway. As the quality of the video captured is corrupted under noisy outdoor environment, a series of preprocessing is required before the features are fed into the network. A vector of nine feature values is extracted to represent whether a vehicle is passing through a lane and this vector serves as input patterns would be used to train the neuro-fuzzy network. The vehicle counting and classification would then be performed by the well-trained network. The novel approach is benchmarked against the MLP and RBF networks. The results of using our proposed neuro-fuzzy network are very encouraging with a high degree of accuracy.

Prediction of internet traffic based on Elman neural network

Abstract: Predicting internet traffic is needed for effective dynamic bandwidth allocation and for quality-of-service (QoS) control strategies implemented at the network edges. In this paper, a method is presented to model and predict the internet traffic based on Elman neural network(Elman-NN). The traffic is viewed as a time series, which is nonlinear and variant functions. An Elman neural network is employed to model the relationship with a satisfactory accuracy, and the Elman NN-based traffic model is used to conduct prediction for the future traffic. The simulation results show that this method is feasible and efficient to model and predict the traffic.

The impact of real time information on transport network routing through intelligent agent-based simulation

Abstract: Advance Traveller Information Systems (ATIS) are considered a promising tool to alleviate traffic congestion and improve road network performance. They provide real time traffic information and route recommendation to road users, in order to increase their ability to choose the best alternative path. Though such systems have reached a high technical standard, their actual impact in traffic pattern and network performance is controversial. The methodology used is based on a Multi Agent Simulation to model how the presence of information influences the driver's reactive behavior and the network efficiency. The case study is the well known network of the Braess' paradox and the specific aim is to find the proper route recommendation strategy to avoid that adding a new road to traffic network may result in increasing the total travel time. Through a software platform able to simulate a virtual road network, where single drivers interact with each other and with the spatial environment according to a defined behavior, that is their reaction to external outputs, two behavioral patterns will be simultaneously considered. The first refers to the driver's path choice among those available for a fixed origin-destination pair; the second refers, once the path is chosen, to the microscopic motion of each vehicle as a function of the leader vehicle along each link of the network. To simulate the presence of drivers equipped with ATIS system and drivers who are not, or equivalently to simulate different reactive behavior to the information provided, it has been used a variable “probability of feedback”. Pattern arrival vehicle flow can be varied together with speed and acceleration of the vehicles. The general purpose of the paper is to contribute to the analysis of the impact of ITS (Intelligent Transport Systems) technology in traffic pattern and network performance. The specific objective is modelling driver's behavior in road networks when real time traffic information is provided. The results show that a proper rate of provided information is able to reduce the effect of the Braess' paradox and that network performance increases when drivers' behavior is affected by their ability to see local traffic conditions.