Title: DDoS attack detection based on global unbiased search strategy bee colony algorithm and artificial neural network

Authors: Qiuting Tian; Dezhi Han; Zhenxin Du

Addresses: College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave, Pudong Xinqu, Shanghai Shi, China ' College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave, Pudong Xinqu, Shanghai Shi, China ' College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave, Pudong Xinqu, Shanghai Shi, China

Abstract: Distributed denial of service (DDoS) attacks are one of the common cyber threats today and are difficult to trace and prevent. The DDoS attack detection method for a single artificial neural network has the problems of slow convergence speed and easy to fall into local optimum. A DDoS attack detection method combining global unbiased search strategy bee colony algorithm and artificial neural network is proposed. This method uses the loss function of the artificial neural network as the objective function of the global unbiased search strategy bee colony algorithm. The optimal weights and thresholds are chosen as the initialisation parameters of the artificial neural network, in order to avoid the artificial neural network falling into a slow convergence speed and local optimum, thereby realising efficient DDoS attack detection. Experimental results show that the DDoS attack detection method has improved the detection accuracy, convergence speed and has good generalisation ability.

Keywords: artificial neural network; ANN; artificial bee colony algorithm; ABC; distributed denial of service; DDoS; loss function; convergence speed; attack detection.

DOI: 10.1504/IJES.2019.102432

International Journal of Embedded Systems, 2019 Vol.11 No.5, pp.584 - 593

Available online: 20 Sep 2019 *

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