A Novel DoS and DDoS Attacks Detection Algorithm Using ARIMA Time Series Model and Chaotic System in Computer Networks


This letter deals with the problem of detecting DoS and DDoS attacks. Initial of all, 2 features including variety of packets and range of supply IP addresses are extracted from network traffics as detection metrics in every minute. Hence, a time series primarily based on the amount of packets is built and normalized using a Box-Cox transformation. An ARIMA model is also employed to predict the quantity of packets in every following minute. Then, the chaotic behavior of prediction error time series is examined by computing the maximum Lyapunov exponent. The native Lyapunov exponent is also calculated as a appropriate indicator for chaotic and nonchaotic errors. Finally, a collection of rules are proposed primarily based on repeatability of chaotic behavior and huge growth in the ratio of number of packets to number of source IP addresses during attack times to classify normal and attack traffics from every alternative. Simulation results show that the proposed algorithm will accurately classify ninety nine.5% of traffic states.

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