Extreme learning machines for intrusion detection systems
作者： Gilles Paiva M. de Farias , Adriano L. I. de Oliveira , George G. Cabral
DOI: 10.1007/978-3-642-34478-7_65
关键词:
摘要: Information is a powerful tool that can be used as competitive advantage to increase market shares, competitiveness and keep products up-to-date. Protecting the information difficult task; intrusion detection systems one of tools great importance for protection computer network infrastructures. IDSs (Intrusion Detection Systems) are help users administrators safe from intruders attacks various natures. Machine learning techniques most popular proposed investigated in literature. This paper focuses on use ELM (Extreme Learning Machine) OS-ELM (Online Sequential ELM) applied IDSs. Some features these methods motivate their building are: (i) easy assignment parameters; (ii) good generalization; (iii) fast online training. The results show easily huge amount data without significant generalization loss.
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