Hybrid Particle Swarm Optimization and Fuzzy C-Means Clustering for Network Intrusion Detection

2018 ◽  
Vol 6 (9) ◽  
pp. 116-122
Author(s):  
Partha Sarathi Bhattacharjee ◽  
Arif Iqbal Mozumder ◽  
Shahin Ara Begum
Keyword(s):  
Particle Swarm Optimization ◽  
Intrusion Detection ◽  
Particle Swarm ◽  
Network Intrusion Detection ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Fuzzy C Means ◽  
Network Intrusion ◽  
Hybrid Particle Swarm Optimization ◽  
Fuzzy C Means Clustering

An Intelligent Network Intrusion Detection System Using Particle Swarm Optimization (PSO) and Deep Network Networks (DNN)

2021 ◽  
Vol 12 (2) ◽  
pp. 57-73
Author(s):  
Preethi D. ◽  
Neelu Khare
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Particle Swarm ◽  
Network Intrusion Detection ◽  
Swarm Optimization ◽  
Network Intrusion ◽  
Proposed Model

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.

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