Title: DDoS attack detection method based on network abnormal behaviour in big data environment

Authors: Jing Chen; Xiangyan Tang; Jieren Cheng; Fengkai Wang; Ruomeng Xu

Addresses: College of Information Science and Technology, Hainan University, Haikou 570228, China ' College of Information Science and Technology, Hainan University, Haikou 570228, China ' College of Information Science and Technology, Hainan University, Haikou 570228, China; Key Laboratory of Internet Information Retrieval of Hainan Province, Hainan University, Haikou 570228, China ' Waite Phillips Hall 3470 Trousdale Parkway, Los Angeles, CA 90089, USA ' College of Information Science and Technology, Hainan University, Haikou 570228, China

Abstract: Distributed denial of service (DDoS) attack becomes a rapidly growing problem with the fast development of the internet. The existing DDoS attack detection methods have time-delay and low detection rate. This paper presents a DDoS attack detection method based on network abnormal behaviour in a big data environment. Based on the characteristics of flood attack, the method filters the network flows to leave only the 'many-to-one' network flows to reduce the interference from normal network flows and improve the detection accuracy. We define the network abnormal feature value (NAFV) to reflect the state changes of the old and new IP addresses of 'many-to-one' network flows. Finally, the DDoS attack detection method based on NAFV real-time series is built to identify the abnormal network flow states caused by DDoS attacks. The experiments show that compared with similar methods, this method has higher detection rate, lower false alarm rate and missing rate.

Keywords: distributed denial of service; DDoS; time series; auto regressive integrated moving average; ARIMA; big data; forecast.

DOI: 10.1504/IJCSE.2020.110182

International Journal of Computational Science and Engineering, 2020 Vol.23 No.1, pp.22 - 30

Received: 29 Mar 2019
Accepted: 20 Nov 2019

Published online: 08 Oct 2020 *

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