Direct Batch Growth Hierarchical Self-organizing Mapping Based on Statistics for Efficient Network Intrusion Detection

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Qu, Xiaofei, Yang, Lin, Guo, Kai, Sun, Meng, Ma, Linru, Feng, Tao, Ren, Shuangyin, Li, Kechao, Ma, Xin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Correlation; Data models; growth hierarchical self-organizing mapping; growth threshold; Intrusion detection; Intrusion detection systems; Mapping; Network intrusion detection; Network topology; Neural networks; Neurons; Representations; statistical enhancement; Topology
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2976810

A new evaluation mechanism was proposed to enhance the representation of data topology in the directed batch growth hierarchical self-organizing mapping. In the proposed mechanism, the growth threshold and the correlation worked in a case-sensitive manner through the statistic calculation of the input data. Since the proposed model enabled a more thorough representation of data topology from both the horizontal and the vertical directions, it naturally held great potential in detecting various traffic attacks. Numerical experiments of network intrusion detection were carried out on the datasets of KDD99 and Moore, where the good performance validated the superiority of the proposed method.