Research on Data Mining of Network Security Hazards Based on Machine Learning Algorithms

With the development and progress of science and technology, an excellent algorithm for data mining of network security hazards is sought, which can effectively discover potential dangers in the network. Based on the XGBoost machine learning algorithm, the differential evolution (DE) algorithm is us...

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Bibliographic Details
Published in:Applied mathematics and nonlinear sciences Vol. 9; no. 1
Main Authors: Wu, Liwan, Yang, Chong
Format: Journal Article
Language:English
Published: Beirut Sciendo 01.01.2024
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Subjects:
05C85
Algorithms
Cyber security
Cybersecurity
Data mining
DE algorithm
DE-XGBoost algorithm
Machine learning
XGBoost algorithm
ISSN:2444-8656, 2444-8656
Online Access:Get full text
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Summary:With the development and progress of science and technology, an excellent algorithm for data mining of network security hazards is sought, which can effectively discover potential dangers in the network. Based on the XGBoost machine learning algorithm, the differential evolution (DE) algorithm is used to train the XGBoost algorithm, and then an optimized DE-XGBoost algorithm is proposed. The construction of an optimal mining and evaluation model is based on this. The DE-XGBoost algorithm’s performance is assessed against cybersecurity hazards using nominal-type posture indicators when data mining cybersecurity hazards. The experimental results show that the DE-XGboost algorithm has the lowest execution time and memory usage during mining, 5min and 82MB respectively, when the number of records in the dataset is 3,500. The DE-XGboost algorithm averages a digging full rate of 92.3%, which is the highest in terms of digging full rate. The posture evaluation experiment uses the DE-XGboost model to predict the posture value that matches the real value with the maximum number of sample points, which is 10 samples. The DE-XGboost algorithm is the perfect choice for cybersecurity data mining due to its optimal performance and best mining effect.
Bibliography:ObjectType-Article-1
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ISSN:2444-8656
2444-8656
DOI:10.2478/amns-2024-0074