A Network Intrusion Detection Model Based on GA-Improved NSA

With the popularization of the Internet, network security issues have also emerged. In response to network security issues, there are certain shortcomings in current network intrusion detection technologies. To improve and optimize this technology, a network intrusion detection model based on geneti...

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Vydané v:Journal of computing and information technology Ročník 31; číslo 2; s. 91 - 105
Hlavný autor: Li, Long
Médium: Journal Article Paper
Jazyk:English
Vydavateľské údaje: Sveuciliste U Zagrebu 01.06.2023
Fakultet elektrotehnike i računarstva Sveučilišta u Zagrebu
Predmet:
Algorithms
Analysis
Computer crimes
Data mining
Data security
Detection
Detectors
Genetic algorithm
Internet
LeNet-5
Machine learning
Methods
Negative selection algorithm
Network intrusion
Safety and security measures
Semiconductor industry
United States
China
ISSN:1330-1136, 1846-3908
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Popis
Shrnutí:With the popularization of the Internet, network security issues have also emerged. In response to network security issues, there are certain shortcomings in current network intrusion detection technologies. To improve and optimize this technology, a network intrusion detection model based on genetic algorithm and improved negative selection algorithm is designed. The generation of detectors in the selection algorithm is replaced by genetic algorithm, and the non-self spatial distribution of detectors is optimized. This paper proposes a network intrusion detection model using a genetic algorithm-improved negative selection algorithm (GA-INSA) and an improved LeNet-5 CNN. The GA enhances detector generation and distribution in NSA while SMOTE handles class imbalance for CNN. Experiments show GA-INSA has over 9% higher accuracy than NSA, SVM and GA-BP across different data sizes. The improved LeNet-5 demonstrates superior accuracy and recall rates over by over 20% baseline LeNet-5. However, more comprehensive evaluation on public datasets, design details on architectures, and discussion around limitations are warranted. The data shows that the designed network intrusion detection model has better performance. The model can provide technical support for network intrusion detection in reality and can enrich the content of network intrusion detection technology.
Bibliografia:314965
ISSN:1330-1136
1846-3908
DOI:10.20532/cit.2023.1005737