A Method to Distinguish DDoS Attack Types Based on RNN

doi:10.3969/j.issn.1671-1122.2022.07.001

Abstract

Abstract:

With the wide application of network technology, there are a variety of network attacks, among which distributed denial of service (DDoS) attacks are more harmful. The 12 types of DDoS attacks are mixed with normal data flows and are difficult to distinguish. The primary problem of defending against DDoS attacks is to distinguish them effectively. For the first time, this paper aimed to distinguish for research purposes attack types. It is proposed a method to distinguish DDoS attack types based on Recurrent neural network(RNN). RNN is a research object, with the modularization research methods and techniques, three types of simple modules are abstracted, and combined to form the RNN-IDDoS model. This model has five layers, three-time steps. Experiments on public datasets show that the proposed model can achieve an accuracy of 99.8%, which is better than the experimental test conclusions of the other three current models and has achieved good discrimination results.

Key words: DDoS attack, type distinguish, RNN

CLC Number:

TP309

FAN Mingyu, LI Ke. A Method to Distinguish DDoS Attack Types Based on RNN[J]. Netinfo Security, 2022, 22(7): 1-8.

Figures/Tables 13

References 18

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