scholarly journals Towards Effective Network Intrusion Detection: A Hybrid Model Integrating Gini Index and GBDT with PSO

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Longjie Li ◽  
Yang Yu ◽  
Shenshen Bai ◽  
Jianjun Cheng ◽  
Xiaoyun Chen
Keyword(s):  
Intrusion Detection ◽  
Hybrid Model ◽  
Gini Index ◽  
Pso Algorithm ◽  
Machine Learning Techniques ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Proposed Model ◽  
Boosted Decision Tree ◽  
Effective Network

In order to protect computing systems from malicious attacks, network intrusion detection systems have become an important part in the security infrastructure. Recently, hybrid models that integrating several machine learning techniques have captured more attention of researchers. In this paper, a novel hybrid model was proposed with the purpose of detecting network intrusion effectively. In the proposed model, Gini index is used to select the optimal subset of features, the gradient boosted decision tree (GBDT) algorithm is adopted to detect network attacks, and the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of GBDT. The performance of the proposed model is experimentally evaluated in terms of accuracy, detection rate, precision, F1-score, and false alarm rate using the NSL-KDD dataset. Experimental results show that the proposed model is superior to the compared methods.

scholarly journals A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1046 ◽  
Author(s):  
Omar Almomani
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Selection Model ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Proposed Model ◽  
Positive Rate

The network intrusion detection system (NIDS) aims to identify virulent action in a network. It aims to do that through investigating the traffic network behavior. The approaches of data mining and machine learning (ML) are extensively used in the NIDS to discover anomalies. Regarding feature selection, it plays a significant role in improving the performance of NIDSs. That is because anomaly detection employs a great number of features that require much time. Therefore, the feature selection approach affects the time needed to investigate the traffic behavior and improve the accuracy level. The researcher of the present study aimed to propose a feature selection model for NIDSs. This model is based on the particle swarm optimization (PSO), grey wolf optimizer (GWO), firefly optimization (FFA) and genetic algorithm (GA). The proposed model aims at improving the performance of NIDSs. The proposed model deploys wrapper-based methods with the GA, PSO, GWO and FFA algorithms for selecting features using Anaconda Python Open Source, and deploys filtering-based methods for the mutual information (MI) of the GA, PSO, GWO and FFA algorithms that produced 13 sets of rules. The features derived from the proposed model are evaluated based on the support vector machine (SVM) and J48 ML classifiers and the UNSW-NB15 dataset. Based on the experiment, Rule 13 (R13) reduces the features into 30 features. Rule 12 (R12) reduces the features into 13 features. Rule 13 and Rule 12 offer the best results in terms of F-measure, accuracy and sensitivity. The genetic algorithm (GA) shows good results in terms of True Positive Rate (TPR) and False Negative Rate (FNR). As for Rules 11, 9 and 8, they show good results in terms of False Positive Rate (FPR), while PSO shows good results in terms of precision and True Negative Rate (TNR). It was found that the intrusion detection system with fewer features will increase accuracy. The proposed feature selection model for NIDS is rule-based pattern recognition to discover computer network attack which is in the scope of Symmetry journal.

scholarly journals On evaluation of Network Intrusion Detection Systems: Statistical analysis of CIDDS-001 dataset using Machine Learning Techniques

2019 ◽  
Author(s):  
Abhishek Verma ◽  
Virender Ranga
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
Network Intrusion Detection ◽  
Detection Systems ◽  
Network Intrusion ◽  
Learning Techniques ◽  
Network Intrusion Detection Systems ◽  
Secure Networks

In the era of digital revolution, a huge amount of data is being generated from different networks on a daily basis. Security of this data is of utmost importance. Intrusion Detection Systems are found to be one the best solutions towards detecting intrusions. Network Intrusion Detection Systems are employed as a defence system to secure networks. Various techniques for the effective development of these defence systems have been proposed in the literature. However, the research on the development of datasets used for training and testing purpose of such defence systems is equally concerned. Better datasets improve the online and offline intrusion detection capability of detection model. Benchmark datasets like KDD 99 and NSL-KDD cup 99 obsolete and do not contain network traces of modern attacks like Denial of Service, hence are unsuitable for the evaluation purpose. In this work, a detailed analysis of CIDDS-001 dataset has been done and presented. We have used different well-known machine learning techniques for analysing the complexity of the dataset. Eminent evaluation metrics including Detection Rate, Accuracy, False Positive Rate, Kappa statistics, Root mean squared error have been used to show the performance of employed machine learning techniques.

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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