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山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (2): 1-10.doi: 10.6040/j.issn.1672-3961.0.2022.136

• 机器学习与数据挖掘 •    下一篇

一种基于改进ReliefF算法的入侵检测模型

刘财辉(),周琪*(),叶晓文   

  1. 赣南师范大学数学与计算机科学学院，江西 赣州 341000
  • 收稿日期:2022-04-11 出版日期:2023-04-22 发布日期:2023-04-21
  • 通讯作者: 周琪 E-mail:liu_caihui@163.com;1203302314@qq.com
  • 作者简介:刘财辉(1979—)，男，江西于都人，教授，博士，主要研究方向为粒计算、机器学习与数据挖掘。E-mail: liu_caihui@163.com

An intrusion detection model based on improved ReliefF algorithm

Caihui LIU(),Qi ZHOU*(),Xiaowen YE   

  1. School of Mathematics and Computer Sciences, Gannan Normal University, Ganzhou 341000, Jiangxi, China
  • Received:2022-04-11 Online:2023-04-22 Published:2023-04-21
  • Contact: Qi ZHOU E-mail:liu_caihui@163.com;1203302314@qq.com

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摘要：

针对现有入侵检测算法中特征提取不充分、未考虑特征权重的影响、模型分类不够精确等问题，提出一种基于改进ReliefF算法的入侵检测模型。通过优化入侵数据特征权重计算，提出改进的ReliefF算法；根据计算特征的Pearson相关系数，建立特征相关性量表。只保留其中一个相关性高的特征，以实现特征的二次优化；对最优特征子集分别使用决策树(decision tree，DT)、k-最近邻(k-nearest neighbor，KNN)、随机森林(random forest，RF)、朴素贝叶斯(naive bayes，NB)和支持向量机(support vector machine，SVM)5种分类器评价该方法的分类性能和准确性。在NSL-KDD和UNSW-NB15两个数据集上的试验结果表明，该方法不仅具有较好的检测性能，还能有效降低特征维度，对分类器的计算复杂度有积极的影响。

关键词: ReliefF算法, 权重优化, 特征选择, 入侵检测, 分类

Abstract:

Aiming at the problems of insufficient feature extraction in the existing intrusion detection algorithms, the influence of feature weights was not considered, and the model classification was not accurate enough, an intrusion detection model based on the improved ReliefF algorithm was proposed. By optimizing the calculation of the feature weight of the intrusion data, an improved algorithm of ReliefF was proposed, based on the Pearson correlation coefficient of the calculated feature, a feature correlation scale was established. Only one of the features with high correlation was retained to realize the secondary optimization of the features, and finally decision tree, k-nearest neighbor, random forest, naive bayes and support vector machine classifier were used to evaluate the classification performance and accuracy. Experimental results on NSL-KDD and UNSW-NB15 data sets showed that this method could not only effectively reduce the feature dimension, but also had better detection performance, which had a positive effect on the computational complexity of the classifier.

Key words: ReliefF algorithm, weight optimization, feature selection, intrusion detection, classification

中图分类号: 

  • TP3-0

图1

基于机器学习的入侵检测模型"

图2

基于改进ReliefF算法的入侵检测模型"

表1

NSL-KDD数据集的数据分布"

类别 样本数量 百分比/%
Nomal 77 054 51.88
Dos 53 385 35.95
Probe 14 077 9.48
U2R 252 0.17
R2L 3 749 2.52
合计 148 517 100.00

表2

NSL-KDD数据集样本选取"

攻击类型 Tr训练集样本个数 Ts测试集样本个数
Nomal 67 343 9 711
Dos 45 927 7 458
Probe 11 656 2 421
U2R 52 200
R2L 995 2 754
合计 125 973 22 544

图3

数据预处理流程图"

表3

数据集中10%和20%数据量的特征权重排序结果"

特征名称 特征权重
10%数据集 20%数据集
dst_host_serror_rate 0.496 5 0.501 0
logged_in 0.457 2 0.438 8
serror_rate 0.428 9 0.424 3
srv_serror_rate 0.373 1 0.382 7
same_srv_rate 0.273 1 0.271 0
dst_host_srv_serror_rate 0.210 4 0.213 6
dst_host_same_srv_rate 0.182 4 0.177 3
dst_host_srv_count 0.145 4 0.139 4
protocol_type 0.131 1 0.135 3
dst_host_count 0.124 0 0.131 2
flag 0.089 6 0.089 4
service 0.085 7 0.086 8
dst_host_same_src_port_rate 0.072 6 0.087 4
count 0.045 8 0.041 7
dst_host_rerror_rate 0.034 8 0.032 5
srv_diff_host_rate 0.025 8 0.032 5
dst_host_diff_srv_rate 0.022 0 0.024 3
dst_host_srv_rerror_rate 0.020 6 0.017 0
rerror_rate 0.016 5 0.018 2
srv_count 0.012 6 0.011 5
is_guest_login 0.010 6 0.011 7
diff_srv_rate 0.010 0 0.010 1
srv_rerror_rate 0.010 0 0.012 0
wrong_fragment 0.007 1 0.006 7
dst_host_srv_diff_host_rate 0.002 5 0.004 2
duration 0.001 9 0.003 0
root_shell 0.001 6 0.000 8
su_attempted 0.000 8 0.000 0
hot 0.000 7 0.000 8
num_shells 0.000 4 0.000 0
num_failed_logins 0.000 0 0.000 0
num_access_files 0.000 0 0.000 0
src_bytes 0.000 0 0.000 0
dst_bytes 0.000 0 0.000 0
land 0.000 0 0.000 0
urgent 0.000 0 0.000 0
num_compromised 0.000 0 0.000 0
num_root 0.000 0 0.000 0
num_file_creations 0.000 0 0.000 0
is_hot_login 0.000 0 0.000 0

表4

特征子集个数"

处理前的特征子集个数 M-ReliefF处理后的特征子集个数 二次优化后的特征子集个数
41 20 20

图4

相关性热力图"

表5

分类样本参数"

样本分类 判断为攻击 判断为正常
攻击样本 TP FN
正常样本 FP TN

表6

不同分类算法的性能指标对比"

数据集名称 分类算法评估准则
ACC/% RP/% F1 /% RD/% RFP/% 算法耗时/s
NSL-KDDDT 98.15 98.45 98.19 97.94 1.62 0.246 6
MR-DT 98.01 98.32 98.05 97.79 1.75 0.172 4
KNN 98.79 98.92 98.81 98.71 1.12 40.345 2
MR-KNN 97.89 97.89 97.94 97.70 1.90 3.193 4
RF 99.44 99.40 99.45 99.50 0.62 5.879 6
MR-RF 96.06 94.28 96.24 98.28 1.49 3.489 3
NB 87.02 84.19 87.89 91.93 18.14 0.210 5
MR-NB 85.13 82.39 86.16 90.29 20.28 0.052 1
SVM 96.70 94.32 96.86 99.55 6.29 2 591.549 7
MR-SVM 95.64 93.58 95.84 98.22 6.63 70.291 3
UNSW-NB15DT 95.03 96.32 96.35 96.38 7.80 2.519 6
MR-DT 94.96 95.83 96.33 96.84 9.09 1.033 7
KNN 93.82 94.47 95.50 96.56 11.99 126.400 7
MR-KNN 94.22 95.45 95.77 96.09 9.75 59.895 2
RF 95.91 96.19 97.02 97.86 0.62 24.542 2
MR-RF 93.71 91.54 95.58 99.99 19.68 10.384 6
NB 87.40 88.74 90.96 93.29 25.12 0.221 6
MR-NB 85.68 84.06 90.30 97.55 39.95 0.118 2
SVM 93.69 91.83 95.54 99.58 18.81 683.028 1
MR-SVM 93.94 92.12 95.74 99.65 18.39 434.906 3

图5

NSL-KDD数据集中不同分类算法的ROC曲线对比图"

图6

UNSW-NB15数据集中不同分类算法的ROC曲线对比图"

表7

本研究方法与其他模型的效果比较"

数据集名称 方法模型 ACC RD RFP
NSL-KDD本研究方法 98.01 97.79 1.75
SSL-3WD[37] 96.10 97.70 2.40
CNN-TWD[38] 96.10 92.30 2.00
Simple-TNN[39] 97.60 98.90 NAN
UNSW-NB15本研究方法 94.96 96.84 9.09
SSL-3WD[37] 94.70 96.30 3.20
ReliefF-P[40] NAN 87.39 15.62
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