Network traffic simulation

About: Network traffic simulation is a research topic. Over the lifetime, 4535 publications have been published within this topic receiving 74606 citations.

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

Real time vehicle speed prediction using a Neural Network Traffic Model

Abstract: Prediction of the traffic information such as flow, density, speed, and travel time is important for traffic control systems, optimizing vehicle operations, and the individual driver. Prediction of future traffic information is a challenging problem due to many dynamic contributing factors. In this paper, various methodologies for traffic information prediction are investigated. We present a speed prediction algorithm, NNTM-SP (Neural Network Traffic Modeling-Speed Prediction) that trained with the historical traffic data and is capable of predicting the vehicle speed profile with the current traffic information. Experimental results show that the proposed algorithm gave good prediction results on real traffic data and the predicted speed profile shows that NNTM-SP correctly predicts the dynamic traffic changes.

Study on network traffic prediction techniques

Abstract: We briefly describe a number of traffic predictors (such as ARIMA, FARIMA, ANN and wavelet-based predictors) and analyze their computational complexity. We compare their performance with MSE, NMSE and computational complexity by simulating the predictors on four wireless network traffic traces and decide the most suitable network traffic predictor based on acceptable performance and accuracy.

Dynamic Power Usage Management Based on Historical Traffic Pattern Data for Network Devices

Abstract: Particular embodiments provide power usage management for network devices according to historical traffic pattern data. Network traffic statistics for traffic flowing through a network device may be determined. A traffic pattern for a time period based on the traffic flowing through the network device is then determined. The network device may then manage power based on the pattern. For example, when a pattern indicates that traffic flowing through the network device is light during a time period, then the network device may operate in a lower power mode, such as a standby mode and when it indicates that there is higher usage, the network device may operate in a normal power mode. In one embodiment, a power usage policy may be determined based on the historical traffic patterns and is automatically enforced by the network device. The power usage policy may also be dynamically adjusted over time based on network traffic statistics.

Research challenges for traffic engineering in software defined networks

Abstract: SDN is an emerging networking paradigm that separates the network control plane from the data forwarding plane with the promise to dramatically improve network resource utilization, simplify network management, reduce operating costs, and promote innovation and evolution. While traffic engineering techniques have been widely exploited for ATM and IP/MPLS networks for performance optimization in the past, the promising SDN networks require novel traffic engineering solutions that can exploit the global network view, network status, and flow patterns/characteristics in order to achieve better traffic control and management. This article discusses the state-of-the-art in traffic engineering for SDN with attention to four cores including flow management, fault tolerance, topology update, and traffic analysis. Challenging issues for SDN traffic engineering solutions are discussed in detail.