融合集成学习技术和PSO-GA算法的特征提取技术的入侵检测方法

doi:10.13229/j.cnki.jdxbgxb.20230751

Abstract

Abstract:

In response to the security issues in industrial networks， a new intrusion detection method is proposed. The specific innovations of the method are divided into two aspects. First， in the process of processing， in order to solve the problem of high dimensionality of the original data， a particle swarm optimize genetic algorithm （PSO-GA） hybrid algorithm with dynamically adjusted parameters was proposed for feature extraction. It successfully screened out a subset of features that are meaningful to model training and accelerated training speed. Secondly， when building a machine learning model， theStacking integrated learning framework is used to generalize the output results of multiple models to improve the overall prediction accuracy. The experimental results on both two datasets show that the detection precision on the publicly available intrusion detection dataset CICDS-2017 has reached 95%， and it also has a 93% precision on a real industrial dataset developed by Lan Turnipseed from the gas pipeline control system.

Key words: computer application, industrial control system, intrusion detection, ensemble learning, feature selection

CLC Number:

TP399

Jun WANG,Chang-fu SI,Kai-peng WANG,Qiang FU. Intrusion detection method based on ensemble learning and feature selection by PSO-GA[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(4): 1396-1405.

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编号	标签	样本数量/个
1	BENIGN	22 767
2	DoS	19 035
3	PortScan	7 946
4	BruteForce	2 767
5	WebAttack	2 180
6	Bot	1 966

标签	缩写（编号）
Normal	Normal（0）
Na?ve Malicious Response Injection	NMRI（1）
Complex Malicious Response Injection	CMRI（2）
Malicious State Command Injection	MSCI（3）
Malicious Parameter Command Injection	MPCI（4）
Malicious Function Code Injection	MFCI（5）
Denial of Service	DOS（6）
Reconnaissance	Recon（7）

	类别1	类别2	…	类别n
类别1	A₁₁	A₁₂	…	A₁_n
类别2	A₂₁	A₂₂	…	A₂_n
?	?	?		?…
类别n	A _n₁	A _n₂	…	A _nn

方法	精确度	召回率	F₁分数	CPU时间/s
决策树	0.925	0.964	0.951	22.8
随机森林	0.862	0.996	0.906	3 min 5
极端梯度提升	0.903	0.997	0.935	4 min 14
堆叠集成	0.936	0.975	0.912	3 min 25
堆叠集成加特征提取	0.951	0.987	0.953	1 min 30

来源	方法	精确度	召回率	F₁分数	类别数
本文	特征提取加堆叠集成	0.951	0.987	0.953	6
文献［24］	MLP	0.871	0.995	0.873	6
文献［25］	DeepGFL	0.948	448	0.531	12
文献［26］	MLP	0.884	0.862	0.872	2
文献［26］	LSTM	0.984	0.898	0.895	2

Intrusion detection method based on ensemble learning and feature selection by PSO-GA

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