2019 · 23 citations · 9 references
ComputingComputer EngineeringComputer ScienceComputer SecurityFeature EngineeringMachine LearningComputer IntrusionDeep LearningIntrusion AnalysisIntrusion DetectionPattern RecognitionRepresentation LearningAccuracyDeep Belief NetworksDeep Learning For Intrusion DetectionFalse Alarm ReductionIntrusion ClassificationMachine Learning For Intrusion DetectionMajority VotingMajority Voting Systems
To leverage the feature extraction of DBN and the fast classification and good generalization of ELM, an improved method of DBN-ELM is proposed for intrusion detection. The improved model uses deep belief network (DBN) to train NSL-KDD dataset and feed them back to the extreme learning machine (ELM) for classification. A classifier is connected at each intermediate level of the DBN-ELM. By majority voting on the output of classifier and ELM, the final output is calculated by integration. Experiments show that the improved model increases the classification confidence and accuracy of the classifier. The model has been benchmarked on the NSL-KDD dataset, and the accuracy of the model has been improved to 97.82%, while the false alarm rate has been reduced to 1.81%. Proposed improved model has been also compared with DBN, ELM, DBN-ELM and achieves competitive accuracy.
9
Extreme learning machine: Theory and applications
Guang-Bin Huang, Qinyu Zhu, Chee‐Kheong Siew · Neurocomputing · 2006 · 13.2K citations
Analog Circuit Incipient Fault Diagnosis Method Using DBN Based Features Extraction
Chaolong Zhang, Yigang He, Lifeng Yuan et al. · IEEE Access · 2018 · 133 citations · Full text