Journal of Shandong University(Engineering Science) ›› 2023, Vol. 53 ›› Issue (2): 1-10.doi: 10.6040/j.issn.1672-3961.0.2022.136

• Machine Learning & Data Mining •     Next Articles

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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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

CLC Number: 

  • TP3-0

Fig.1

Intrusion detection model based on machine learning"

Fig.2

Intrusion detection model based on improved ReliefF algorithm"

Table 1

Data distribution of NSL-KDD dataset"

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

Table 2

NSL-KDD data set sample selection"

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

Fig.3

Data preprocessing flowchart"

Table 3

Feature weight sorting results for 10% and 20% of the data volume in the data set"

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

Table 4

Number of characteristic subsets"

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

Fig.4

Correlation heat map"

Table 5

Classification sample parameters"

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

Table 6

Comparison of performance indicators of different classification algorithms"

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

Fig.5

Comparison of ROC curves of different classification algorithms in the NSL-KDD dataset"

Fig.6

Comparison of ROC curves of different classification algorithms in the UNSW-NB15 dataset"

Table 7

Comparison of results of this research method and other models 单位: %"

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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