Title: Abnormal network data mining model based on deep training learning

Authors: Xiaoling Jiang; Hui Zhang; Jiaming Xu; Weicheng Wu; Xingyong Xie

Addresses: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai'an 223003, China ' Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai'an 223003, China ' Faculty of Humanity, Huaiyin Institute of Technology, Huai'an 223003, China ' Office of Academic Affairs, Huaiyin Institute of Technology, Huai'an 223003, China ' Faculty of Chemical Engineering, Huaiyin Institute of Technology, Huai'an 223003, China

Abstract: Aiming at the problems of low detection efficiency and poor clustering effect in traditional abnormal network data mining process, an abnormal data mining model based on kernel extreme learning machine and particle swarm optimisation is proposed. The enhanced local linear embedding algorithm is used to extract the features of abnormal network data, and the required feature dimensions are extracted repeatedly to obtain the corresponding features of target network data. K-means algorithm is introduced to cluster the target network data to increase the identification of data mining. By improving the particle swarm optimisation algorithm to optimise the parameters of the kernel limit learning machine, the final abnormal data mining results are the best. The experimental results show that the proposed method has high detection efficiency and good clustering effect, which fully proves the superiority of the proposed method and lays a foundation for the progress of abnormal network data mining technology.

Keywords: local linear embedding algorithm; target network data characteristics; k-means algorithm; improved particle swarm optimisation algorithm.

DOI: 10.1504/IJIPT.2020.110314

International Journal of Internet Protocol Technology, 2020 Vol.13 No.4, pp.228 - 236

Received: 28 Mar 2019
Accepted: 16 Jun 2019
Published online: 14 Oct 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article