Forecasting network traffic at large time scale by using dual-related method

Tang, Liangrui; Du, Shimo; Ji, Shiyu

doi:10.1109/fskd.2016.7603372

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…Katris and Sophia, 2015). In other work that has used ANN, it was found that ANN was a competitive model to use and was superior to the classical regression model (Tang et al, 2016). In our work, we chose the ANN to build our model and use it for network traffic prediction as a first stage.…”

Section: Network Traffic Predictionmentioning

confidence: 99%

A Novel Hybrid Method for Network Anomaly Detection Based on Traffic Prediction and Change Point Detection

Alkasassbeh¹

2018

Journal of Computer Science

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In recent years, computer networks have become more and more advanced in terms of size, applications, complexity and level of heterogeneity. Moreover, availability and performance are important issues for end users. New types of cyber-attacks that can affect and damage network performance and availability are constantly emerging and some threats, such as Distributed Denial of Service (DDoS) attacks, can be very dangerous and cannot be easily prevented. In this study, we present a novel hybrid approach to detecting a DDoS attack by means of monitoring abnormal traffic in the network. This approach reads traffic data and from that it is possible to build a model, by means of which future data may be predicted and compared with observed data, in order to detect any abnormal traffic. This approach combines two methods: traffic prediction and changing detection. To the best of our knowledge, such a combination has never been used in this area before. The approach achieved a highly significant accuracy rate of 98.3% and sensitivity was 100%, which means that all potential attacks are detected and prevented from penetrating the network system.

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Section: Network Traffic Predictionmentioning

confidence: 99%