Developing a multi-level intrusion detection system using hybrid-DBN

Autor: Ahmet Ali Süzen
Rok vydání: 2020
Předmět:
Zdroj: Journal of Ambient Intelligence and Humanized Computing. 12:1913-1923
ISSN: 1868-5145
1868-5137
DOI: 10.1007/s12652-020-02271-w
Popis: In this study, a hybrid deep belief network (DBN) cyber intrusion detection system was proposed to provide a secure network by controlling network traffic in Industrial control systems (ICS). The disadvantages of DBN have been analyzed and improved to create attack detectors in network traffic. The output is combined with Softmax Regression for effective intrusion detection and classification detection. Training and testing of the hybrid DBN model were carried out with the actual and original data set generated by ICS. DBNs are a much-preferred approach for detecting malicious attacks in network traffic. In instances where there is a lot of data, it is important to select the most appropriate structure for the DBN model. Therefore, in the model the hidden layers are updated by contrastive divergence (CD), and the output layer is combined with the Softmax classifier. The proposed model architecture has proved successful in many limitations, such as the complexity and size of training data. The proposed hybrid DBN model provided 99.72% accuracy in intrusion detection and classification. These results showed that the model achieved better performance than the existing intrusion detection system (IDS). It also provided approximately 5% more accuracy improvements with the hybrid model than with older DBN-based systems.
Databáze: OpenAIRE
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