DDoS Detection Algorithm Based on Preprocessing Network Traffic Predicted Method and Chaos Theory

2013 ◽  
Vol 17 (5) ◽  
pp. 1052-1054 ◽  
Author(s):  
Yonghong Chen ◽  
Xinlei Ma ◽  
Xinya Wu
Keyword(s):  
Chaos Theory ◽  
Network Traffic ◽  
Detection Algorithm ◽  
Ddos Detection

scholarly journals ForChaos: Real Time Application DDoS Detection Using Forecasting and Chaos Theory in Smart Home IoT Network

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Andria Procopiou ◽  
Nikos Komninos ◽  
Christos Douligeris
Keyword(s):  
Chaos Theory ◽  
Smart Home ◽  
Detection Algorithm ◽  
Home Networks ◽  
Ddos Attacks ◽  
Dos Attacks ◽  
Network Systems ◽  
Ddos Detection ◽  
Series Of Experiments ◽  
Iot Devices

Recently, D/DoS attacks have been launched by zombie IoT devices in smart home networks. They pose a great threat to network systems with Application Layer DDoS attacks being especially hard to detect due to their stealth and seemingly legitimacy. In this paper, we propose ForChaos, a lightweight detection algorithm for IoT devices, which is based on forecasting and chaos theory to identify flooding and DDoS attacks. For every time-series behaviour collected, a forecasting-technique prediction is generated, based on a number of features, and the error between the two values is calculated. In order to assess the error of the forecasting from the actual value, the Lyapunov exponent is used to detect potential malicious behaviour. In NS-3 we evaluate our detection algorithm through a series of experiments in flooding and slow-rate DDoS attacks. The results are presented and discussed in detail and compared with related studies, demonstrating its effectiveness and robustness.

Network traffic anomaly detection algorithm using mahout classifier

2019 ◽  
Vol 37 (1) ◽  
pp. 137-144 ◽  
Author(s):  
Hua Peng ◽  
Liang Liu ◽  
Jiayong Liu ◽  
Johnwb R. Lewis
Keyword(s):  
Anomaly Detection ◽  
Network Traffic ◽  
Detection Algorithm ◽  
Traffic Anomaly ◽  
Traffic Anomaly Detection

A Detection Algorithm to Anomaly Network Traffic Based on Spectral Kurtosis Analysis

2013 ◽  
Vol 765-767 ◽  
pp. 1461-1464 ◽  
Author(s):  
Ding De Jiang ◽  
Cheng Yao ◽  
Wei Han Zhang ◽  
Zheng Zheng Xu
Keyword(s):  
Network Traffic ◽  
Detection Algorithm ◽  
Time Frequency ◽  
Spectral Kurtosis ◽  
Analysis Theory ◽  
Time Signals ◽  
Local Properties ◽  
Simulation Results ◽  
Detailed Nature ◽  
New Time

This paper presents a detection algorithm for anomaly network traffic, which is based on spectral kurtosis analysis. Firstly, we turn network traffic into time-frequency signals at different scales. These time-frequency signals hold the more detailed nature corresponding to different scales. Secondly, the time-frequency signals at different scales are transformed into a series of new time signals by time-frequency analysis theory. These new time signals hold obvious narrowband nature and embody the local properties of network traffic. Thirdly, we calculate the spectral kurtosis values of the new time signals and then perform the feature extractions. As a result, the abnormal network traffic can be correctly identified. Simulation results show that our algorithm is feasible and promising.

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