Rough Estimator Based Asynchronous Distributed Super Points Detection on High Speed Network Edge

Super points detection plays an important role in network research and application. With the increase of network scale, distributed super points detection has become a hot research topic. The key point of super points detection in a multi-node distributed environment is how to reduce communication overhead. Therefore, this paper proposes a three-stage communication algorithm to detect super points in a distributed environment, Rough Estimator based Asynchronous Distributed super points detection algorithm (READ). READ uses a lightweight estimator, the Rough Estimator (RE), which is fast in computation and takes less memory to generate candidate super points. Meanwhile, the famous Linear Estimator (LE) is applied to accurately estimate the cardinality of each candidate super point, so as to detect the super point correctly. In READ, each node scans IP address pairs asynchronously. When reaching the time window boundary, READ starts three-stage communication to detect the super point. This paper proves that the accuracy of READ in a distributed environment is no less than that in the single-node environment. Four groups of 10 Gb/s and 40 Gb/s real-world high-speed network traffic are used to test READ. The experimental results show that READ not only has high accuracy in a distributed environment, but also has less than 5% of communication burden compared with existing algorithms.

RT-SAD: Real-Time Sketch-Based Adaptive DDoS Detection for ISP Network

With the great changes in network scale and network topology, the difficulty of DDoS attack detection increases significantly. Most of the methods proposed in the past rarely considered the real-time, adaptive ability, and other practical issues in the real-world network attack detection environment. In this paper, we proposed a real-time adaptive DDoS attack detection method RT-SAD, based on the response to the external network when attacked. We designed a feature extraction method based on sketch and an adaptive updating algorithm, which makes the method suitable for the high-speed network environment. Experiment results show that our method can detect DDoS attacks using sampled Netflowunder high-speed network environment, with good real-time performance, low resource consumption, and high detection accuracy.

A flow control algorithm for high-speed computer networks

Developing an effective flow control algorithm to avoid congestion is a hot topic in computer network society. This paper gives a mathematical model for general network at first, and then discrete control theory is proposed as a key tool to design a new flow control algorithm for congestion avoidance in high speed network, the proposed algorithm assures the stability of network system. The simulation results show that the proposed method can adjust the sending rate and queue level in buffer rapidly and effectively. The method is easy to implement and apply to high-speed computer network.

TECHNOLOGICAL TOOLS FOR ENHANCING TEACHING AND LEARNING PROCESS

In recent years technology has changed dramatically. Due to the increasing need and accessibility of technology, it has expanded the toolbox and provides opportunities to the teachers for using technology. Computer devices are considered to be the more powerful tools which have come in various forms. The internet helps connect those devices and can connect students in the classrooms, through schools or around the world. Now a day, there is an availability of computers and interactive boards in schools, and the schools were connected as well as to the whole world providing high-speed network connectivity. Technology present in schools in the form of tablet devices, smartphones, and laptop computers are now used as a part and parcel of the teaching-learning process. The objective of the current study is to provide information about various technological tools that help enhance the teaching and learning process. These tools can help the teachers in creating, manipulating, using and sharing information over the networks.

Traffic Flows Analysis in High-Speed Computer Networks Using Time Series

This article explores the required amount of time series points from a high-speed traffic network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series, followed by addressing the minimum amount of points required to obtain accurate estimates of the Hurst exponent in real-time. The methodology addresses the exhaustive analysis of the Hurst exponent considering bias behavior, standard deviation, mean square error, and convergence using fractional Gaussian noise signals with stationary increases. Our results show that the Whittle estimator successfully estimates the Hurst exponent in series with few points. Based on the results obtained, a minimum length for the time series is empirically proposed. Finally, to validate the results, the methodology is applied to real traffic captures in a high-speed network based on the IEEE 802.3ab standard.