Fuzzy min–max neural network and particle swarm optimization based intrusion detection system

2016 ◽  
Vol 23 (4) ◽  
pp. 907-918 ◽  
Author(s):  
Chandrashekhar Azad ◽  
Vijay Kumar Jha
2021 ◽  
Vol 12 (2) ◽  
pp. 57-73
Author(s):  
Preethi D. ◽  
Neelu Khare

Network intrusion detection system (NIDS) plays a major role in ensuring network security. In this paper, the authors propose a PSO-DNN-based intrusion detection system. The correlation-based feature selection (CFS) applied for feature selection with particle swarm optimization (PSO) as search method and deep neural networks (DNN) for classification of network intrusions. The Adam optimizer is applied for optimizing the learning rate, and softmax classifier is used for classification. The experimentations were duly conducted on the standard benchmark NSL-KDD dataset. The proposed model is validated using 10-fold cross-validation and evaluated using the performance metrics such as accuracy, precision, recall, and F1-score. Also, the results are also compared with DNN and CFS+DNN. The experimental results show that the proposed model performs better compared with other methods considered for comparison.


2021 ◽  
pp. 579-588
Author(s):  
Siti Norwahidayah Wahab ◽  
Noor Suhana Sulaiman ◽  
Noraniah Abdul Aziz ◽  
Nur Liyana Zakaria ◽  
Ainal Amirah Abd Aziz

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