doi: 10.3934/dcdss.2020263

Density-based semi-supervised classification for intrusion detection

1. 

College of economics management, Shangluo University, Shangluo 726000, China

2. 

College of mathematics and computer application, Shangluo University, Shangluo 726000, China

*Corresponding author: Ning Liu

Received  April 2019 Revised  May 2019 Published  February 2020

In order to improve the classification performance of intrusion detection problems with only a small number of labeled samples, semi-supervised learning is applied into the field of network intrusion. A semi-supervised classification method based on data density (SSC-density) is proposed to implement intrusion detection and solve network intrusion detection problem with fewer label samples. Firstly, the intrusion detection data is numerically numbered and normalized; secondly, the density of each sample is calculated, and the data samples are divided into security points, boundary points and noise points based on the density, so as to determine the spatial structure of the data; thirdly, different strategies are used for semi-supervised learning on different types of samples to mine the implicit information of unlabeled samples and expand the number of labeled samples. Specifically, deleting noise points, semi-supervised learning is firstly performed on the data set composed of security points, and then semi-supervised learning is performed on the data set composed of boundary points. Finally, the labeled samples are trained to generate the final classifier to realize the intrusion detection of network data. Experiments are carried out on KDD CUP 99 data set, the experimental result shows that the proposed algorithm has good classification performance.

Citation: Ning Liu, Jianhua Zhao. Density-based semi-supervised classification for intrusion detection. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020263
References:
[1]

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W. Qing-Tao and Z. Q. Shao, Survey on intrusion detection techniques, Application Research of Computers, 22 (2005), 11-44.   Google Scholar

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C. SiS. Zhu and Y. Yan, Robust visual tracking via online semi-supervised co-boosting, Multimedia Systems, 22 (2016), 297-313.   Google Scholar

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D. TuiaM. VolpiM. Trolliet and G. Camps-Valls, Semisupervised manifold alignment of multimodal remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 52 (2014), 7708-7720.  doi: 10.1109/TGRS.2014.2317499.  Google Scholar

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L. ZhangL. Wang and W. Lin, Semisupervised biased maximum margin analysis for interactive image retrieval, IEEE Transactions on Image Processing, 21 (2012), 2294-2308.  doi: 10.1109/TIP.2011.2177846.  Google Scholar

[29]

Y. G. ZhangW. ZhangX. R. Xue and X. J. Yang, Scada intrusion detection system based on self-learning semi-supervised one-class support vector machine, Metallurgical Industry Automation, 37 (2013), 1-5.   Google Scholar

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Q. ZhiquanY. Tian and Y. Shi, Laplacian twin support vector machine for semi-supervised classification, Neural Networks, 35 (2012), 46-53.   Google Scholar

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L. ZhuangZ. ZhouS. GaoJ. YinZ. Lin and Y. Ma, Label information guided graph construction for semi-supervised learning, IEEE Transactions on Image Processing, 26 (2017), 4182-4192.  doi: 10.1109/TIP.2017.2703120.  Google Scholar

show all references

References:
[1]

A. AppiceP. Guccione and D. Malerba, Transductive hyperspectral image classification: Toward integrating spectral and relational features via an iterative ensemble system, Machine Learning, 103 (2016), 343-375.  doi: 10.1007/s10994-016-5559-7.  Google Scholar

[2]

R. A. R. AshfaqX. Z. WangJ. Z. HuangH. Abbas and Y. L. He, Fuzziness based semi-supervised learning approach for intrusion detection system, Information Sciences An International Journal, 378 (2017), 484-497.  doi: 10.1016/j.ins.2016.04.019.  Google Scholar

[3]

B. BasavanagoudV. R. Desai and S. Patil, (B, A)- connectivity index of graphs, Applied Mathematics and Nonlinear Sciences, 2 (2017), 21-30.  doi: 10.21042/AMNS.2017.1.00003.  Google Scholar

[4]

M. Belkin and P. Niyogi, Semi-supervised learning on riemannian manifolds: Theoretical advances in data clustering (guest editors: Nina mishra and rajeev motwani), Machine Learning, 56 (2004), 209-239.   Google Scholar

[5]

A. Blum and T. Mitchell, Combining labeled and unlabeled data with co-training, in Conference on Computational Learning Theory, 1998, 92–100. doi: 10.1145/279943.279962.  Google Scholar

[6]

T. BrownS. DuH. Eruslu and F. J. Sayas, Analysis of models for viscoelastic wave propagation, Applied Mathematics and Nonlinear Sciences, 3 (2018), 55-96.  doi: 10.21042/AMNS.2018.1.00006.  Google Scholar

[7]

W. L. CaldasJ. P. P. Gomes and D. P. P. Mesquita, Fast co-mlm: An efficient semi-supervised co-training method based on the minimal learning machine, New Generation Computing, 36 (2018), 41-58.  doi: 10.1007/s00354-017-0027-x.  Google Scholar

[8]

T. Dash, A study on intrusion detection using neural networks trained with evolutionary algorithms, Soft Computing, 21 (2017), 2687-2700.  doi: 10.1007/s00500-015-1967-z.  Google Scholar

[9]

J. J. Davis and A. J. Clark, Data preprocessing for anomaly based network intrusion detection: A review, Computers and Security, 30 (2011), 353-375.  doi: 10.1016/j.cose.2011.05.008.  Google Scholar

[10]

X. DuanA. ZhongL. Ying and C. Jia, Intrusion detection method for program vulnerability via library calls, Wuhan University Journal of Natural Sciences, 12 (2007), 126-130.  doi: 10.1007/s11859-006-0237-4.  Google Scholar

[11]

S. ForrestS. A. Hofmeyr and A. Somayaji, Computer immunology, Immunological Reviews, 216 (2007), 176-197.   Google Scholar

[12]

W. Gao and W. Wang, A tight neighborhood union condition on fractional (g, f, n ', m)-critical deleted graphs, Applied Mathematics and Nonlinear Sciences, 149 (2018), 291-298.  doi: 10.4064/cm6959-8-2016.  Google Scholar

[13]

G. HuangS. SongJ. N. D. Gupta and C. Wu, Semi-supervised and unsupervised extreme learning machines, IEEE Transactions on Cybernetics, 44 (2017), 2405-2417.  doi: 10.1109/TCYB.2014.2307349.  Google Scholar

[14]

M. IdhammadK. Afdel and M. Belouch, Semi-supervised machine learning approach for ddos detection, Applied Intelligence, 48 (2018), 3193-3208.  doi: 10.1007/s10489-018-1141-2.  Google Scholar

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J. Jin and W. Mi, An aimms-based decision-making model for optimizing the intelligent stowage of export containers in a single bay, Discrete and Continuous Dynamical Systems Series S, 12 (2019), 1101-1115.   Google Scholar

[16]

M. Li and Z. H. Zhou, Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 37 (2007), 1088-1098.  doi: 10.1109/TSMCA.2007.904745.  Google Scholar

[17]

W. LiW. MengX. Luo and L. F. Kwok, Mvpsys : Toward practical multi-view based false alarm reduction system in network intrusion detection, Computers and Security, 60 (2016), 177-192.  doi: 10.1016/j.cose.2016.04.007.  Google Scholar

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J. LongW. ZhaoF. Zhu and Z. Cai, Active learning to defend poisoning attack against semi-supervised intrusion detection classifier, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 19 (2011), 93-106.  doi: 10.1142/S0218488511007362.  Google Scholar

[19]

P. F. Marteau, Sequence covering for efficient host-based intrusion detection, IEEE Transactions on Information Forensics and Security, 14 (2019), 994-1006.  doi: 10.1109/TIFS.2018.2868614.  Google Scholar

[20]

W. Qing-Tao and Z. Q. Shao, Survey on intrusion detection techniques, Application Research of Computers, 22 (2005), 11-44.   Google Scholar

[21]

C. SiS. Zhu and Y. Yan, Robust visual tracking via online semi-supervised co-boosting, Multimedia Systems, 22 (2016), 297-313.   Google Scholar

[22]

D. TuiaM. VolpiM. Trolliet and G. Camps-Valls, Semisupervised manifold alignment of multimodal remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 52 (2014), 7708-7720.  doi: 10.1109/TGRS.2014.2317499.  Google Scholar

[23]

M. Usha and P. Kavitha, Anomaly based intrusion detection for 802.11 networks with optimal features using svm classifier, Wireless Networks, 23 (2016), 2431-2446.  doi: 10.1007/s11276-016-1300-5.  Google Scholar

[24]

C. Warrender, S. Forrest and B. Pearlmutter, Detecting intrusions using system calls: Alternative data models, in IEEE Symposium on Security and Privacy, 2002. doi: 10.1109/SECPRI.1999.766910.  Google Scholar

[25]

Z. XueY. Shang and A. Feng, Semi-supervised outlier detection based on fuzzy rough c-means clustering, Mathematics and Computers in Simulation, 80 (2010), 1911-1921.  doi: 10.1016/j.matcom.2010.02.007.  Google Scholar

[26]

Y. YuY. XinUo zumi and Ta kashi, A hierarchical image annotation method based on svm and semi-supervised em, Acta Automatica Sinica, 36 (2010), 960-967.   Google Scholar

[27]

W. Yi and L. Tao, Improving semi-supervised co-forest algorithm in evolving data streams, Applied Intelligence, 48 (2018), 3248-3262.   Google Scholar

[28]

L. ZhangL. Wang and W. Lin, Semisupervised biased maximum margin analysis for interactive image retrieval, IEEE Transactions on Image Processing, 21 (2012), 2294-2308.  doi: 10.1109/TIP.2011.2177846.  Google Scholar

[29]

Y. G. ZhangW. ZhangX. R. Xue and X. J. Yang, Scada intrusion detection system based on self-learning semi-supervised one-class support vector machine, Metallurgical Industry Automation, 37 (2013), 1-5.   Google Scholar

[30]

J. ZhenS. Zhang and J. Zeng, A hybrid generative/discriminative method for semi-supervised classification, Knowledge-Based Systems, 37 (2013), 137-145.   Google Scholar

[31]

Q. ZhiquanY. Tian and Y. Shi, Laplacian twin support vector machine for semi-supervised classification, Neural Networks, 35 (2012), 46-53.   Google Scholar

[32]

L. ZhuangZ. ZhouS. GaoJ. YinZ. Lin and Y. Ma, Label information guided graph construction for semi-supervised learning, IEEE Transactions on Image Processing, 26 (2017), 4182-4192.  doi: 10.1109/TIP.2017.2703120.  Google Scholar

Figure 1.  The flow chart of SSC-density
Table 1.  Experimental data set
Attack categories Type of attack training set Test set
Normal normal 8000 4000
DOS back 900 400
neptune 3500 2000
smurf 2100 1000
Total 6500 3400
R2L guess_passwd 53 40
Total 73 40
U2R buffer_overflow 30 22
Total 30 22
Probe ipsweep 500 180
portsweep 500 200
satan 417 158
Total 1397 538
Attack categories Type of attack training set Test set
Normal normal 8000 4000
DOS back 900 400
neptune 3500 2000
smurf 2100 1000
Total 6500 3400
R2L guess_passwd 53 40
Total 73 40
U2R buffer_overflow 30 22
Total 30 22
Probe ipsweep 500 180
portsweep 500 200
satan 417 158
Total 1397 538
Table 2.  Confusion matrix
Category Actual positive class Actual negative class
Experimental positive class TP FN
Experimental negative class FP TN
Category Actual positive class Actual negative class
Experimental positive class TP FN
Experimental negative class FP TN
Table 3.  Experimental result (SVM, N = 5%)
data set method1 method2 method3 Our algorithm
normal 0.7554 0.8282 0.9057 0.9282
abnormal 0.6587 0.7640 0.8874 0.8934
data set method1 method2 method3 Our algorithm
normal 0.7554 0.8282 0.9057 0.9282
abnormal 0.6587 0.7640 0.8874 0.8934
Table 4.  Experimental result (SVM, N = 10%)
data set method1 method2 method3 Our algorithm
normal 0.7845 0.8572 0.9274 0.9517
abnormal 0.7012 0.7774 0.9045 0.9354
data set method1 method2 method3 Our algorithm
normal 0.7845 0.8572 0.9274 0.9517
abnormal 0.7012 0.7774 0.9045 0.9354
Table 5.  Experimental result (SVM, N = 15%)
data set method1 method2 method3 Our algorithm
normal 0.8117 0.8819 0.9556 0.9720
abnormal 0.7157 0.8447 0.9384 0.9402
data set method1 method2 method3 Our algorithm
normal 0.8117 0.8819 0.9556 0.9720
abnormal 0.7157 0.8447 0.9384 0.9402
Table 6.  Experimental result (RBF, N = 5%)
data set method1 method2 method3 Our algorithm
normal 0.7014 0.8041 0.8734 0.9047
abnormal 0.6347 0.7513 0.8524 0.8786
data set method1 method2 method3 Our algorithm
normal 0.7014 0.8041 0.8734 0.9047
abnormal 0.6347 0.7513 0.8524 0.8786
Table 7.  Experimental result (RBF, N = 10%)
data set method1 method2 method3 Our algorithm
normal 0.7328 0.8312 0.9324 0.9437
abnormal 0.6847 0.8090 0.8878 0.9275
data set method1 method2 method3 Our algorithm
normal 0.7328 0.8312 0.9324 0.9437
abnormal 0.6847 0.8090 0.8878 0.9275
Table 8.  Experimental result (RBF, N = 15%)
data set method1 method2 method3 Our algorithm
normal 0.7925 0.8675 0.9425 0.9734
abnormal 0.7089 0.8287 0.9074 0.9355
data set method1 method2 method3 Our algorithm
normal 0.7925 0.8675 0.9425 0.9734
abnormal 0.7089 0.8287 0.9074 0.9355
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