Network Security Based on Improved Genetic Algorithm and Weighted Error Back-Propagation Algorithm

In order to solve the problem of feature selection and local optimal solution in the field of network security, a network security protection model based on improved genetic algorithm and weighted error back-propagation algorithm is proposed. The model combines the dynamic error weight and adaptive...

Full description

Saved in:
Bibliographic Details
Published in:International journal of advanced computer science & applications Vol. 15; no. 11
Main Author: Liang, Junjuan
Format: Journal Article
Language:English
Published: West Yorkshire Science and Information (SAI) Organization Limited 2024
Subjects:
Accuracy
Adaptive algorithms
Adaptive learning
Artificial intelligence
Back propagation
Clustering
Communications traffic
Computer science
Data integrity
Efficiency
Errors
Experiments
Feature selection
Genetic algorithms
Machine learning
Network reliability
Neural networks
Optimization
Propagation
Propagation modes
Resource management
Security
ISSN:2158-107X, 2156-5570
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In order to solve the problem of feature selection and local optimal solution in the field of network security, a network security protection model based on improved genetic algorithm and weighted error back-propagation algorithm is proposed. The model combines the dynamic error weight and adaptive learning rate of the weighted error back-propagation algorithm to improve the learning ability of the model in dealing with classification imbalance and dynamic attack mode. In addition, the global search capability of genetic algorithm is utilized to optimize the feature selection process and automatically adjust the hyperparameter settings. The experimental results show that the proposed model has an average accuracy of 96.7%, a recall rate of 93.3% and an F1 value of 0.91 on the CIC-IDS-2017 dataset, which has significant advantages over traditional detection methods. In many experiments, the accuracy of normal data is up to 99.97%, the accuracy of known abnormal behavior data is 99.31%, and the accuracy of unknown abnormal behavior data is 98.13%. These results show that this method has high efficiency and reliability when dealing with complex network traffic, and provides a new idea and method for network security protection research.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2024.0151121