Application of Support Vector Machine Optimized by Artificial Bee Colony Algorithm in Network Security

LIANG Hai-jun

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Application of Support Vector Machine Optimized by Artificial Bee Colony Algorithm in Network Security

更新时间：2026-09-04

- Application of Support Vector Machine Optimized by Artificial Bee Colony Algorithm in Network Security
- Vol. 30, Issue 2, Pages: 95-98(2013)
- 作者机构：
  
  淮海工学院机构信息中心,江苏,连云港,222005
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2013
- 稿件说明：
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梁海军. 人工蜂群优化支持向量机算法在网络安全中的应用[J]. 微电子学与计算机, 2013, 30(2): 95-98.

LIANG Hai-jun. Application of Support Vector Machine Optimized by Artificial Bee Colony Algorithm in Network Security[J]. 2013, 30(2): 95-98.
梁海军. 人工蜂群优化支持向量机算法在网络安全中的应用[J]. 微电子学与计算机, 2013, 30(2): 95-98. DOI：

LIANG Hai-jun. Application of Support Vector Machine Optimized by Artificial Bee Colony Algorithm in Network Security[J]. 2013, 30(2): 95-98. DOI：

摘要

针对支持向量机(SVM)性能的影响

探讨了人工蜂群算法(ABC)对SVM参数优化方法

建立了SVM参数优化模型

并将其用于网络安全中的网络入侵模型中.采用KDD 1999数据集进行仿真实验

验证了方法的有效性

结果表明

与遗传算法等传统优化算法相比

ABC优化的SVM有效地降低运行时间

可以获得更高的网络入侵检测率.

Abstract

Parameters of support vector machine(SVM) is the key factor that impacts its performance.A parameter optimization method for SVM using artificial bee colony algorithm(ABC) is discussed and a parameter optimization model is established.The proposed algorithms performance is tested by KDD 1999 data.The results show that the proposed algorithm decreased the running time efficiently and improved the accuracy of network intrusion detection.

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Related Institution

School of Internet of Things Engineering, Jiangnan University

School of Optical and Electronic Information,Huazhong University of Science and Technology

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