A hybrid technique using binary particle swarm optimization and decision tree pruning for network intrusion detection

2017 ◽  
Vol 21 (1) ◽  
pp. 667-680 ◽  
Author(s):  
Arif Jamal Malik ◽  
Farrukh Aslam Khan
Keyword(s):  
Particle Swarm Optimization ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Particle Swarm ◽  
Network Intrusion Detection ◽  
Binary Particle Swarm Optimization ◽  
Hybrid Technique ◽  
Swarm Optimization ◽  
Network Intrusion ◽  
Tree Pruning

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.

SOM and PSO Based Alerts Clustering in Intrusion Detection System

2013 ◽  
Vol 401-403 ◽  
pp. 1453-1457 ◽  
Author(s):  
Yong Wen Jing ◽  
Li Fen Li
Keyword(s):  
Particle Swarm Optimization ◽  
Intrusion Detection ◽  
Learning Algorithm ◽  
Detection System ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Self Organizing Maps ◽  
Swarm Optimization ◽  
Network Intrusion ◽  
Comparative Results

With the growing deployment of host and network intrusion detection systems (IDSs), thousands of alerts are generally generated from them per day. Managing these alerts becomes critically important. In this paper, a hybrid alert clustering method based on self-Organizing maps (SOM) and particle swarm optimization (PSO) is presented. We firstly select the important features through binary particle swarm optimization (BPSO) and mutual information (MI) and get a dimension reduced dataset. SOM is used to cluster the dataset. PSO is used to evolve the weights for SOM to improve the clustering result. The algorithm is based on a type of unsupervised machine learning algorithm that infers relationships from data without the need to train the algorithm with expertly labelled data. The approach is validated using the 2000 DARPA intrusion detection datasets and comparative results between the canonical SOM and our scheme are presented.

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