Improving Performance and Convergence Rates in Multi-Layer Feed Forward Neural Network Intrusion Detection Systems: A Review of the Literature
作者： Loye Lynn Ray , Henry Felch
DOI: 10.4018/IJSITA.2014070102
关键词:
摘要: Today's anomaly-based network intrusion detection systems IDSs are plagued with detecting new and unknown attacks. The review of the literature builds ideas for researching problem these attacks using multi-layered feed forward neural MLFFNN IDSs. scope paper focused on a from primarily 2008 to present found in peer-review scholarly journals. A key word search was used compare contrast find strengths, weaknesses gaps. significance research that further work is needed improve performance convergence rates This contributes area by looking at effects architecture, algorithms, input data
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