Fusion of Improved Polynomial Regression and Random Forest Network Intrusion Detection Model
DOI: 10.23977/acss.2024.080220 | Downloads: 16 | Views: 406
Author(s)
Jie Zhang 1, Tao Hong 1, Xingxiang Lin 1
Affiliation(s)
1 School of Computer and Artificial Intelligence, Huanghuai University, Zhumadian, Henan, China
Corresponding Author
Jie ZhangABSTRACT
With the advent of the era of big data and the continuous development of the Internet, it is becoming more and more important to maintain network information security. Faced with the high frequency of network intrusion, network intrusion detection system has become a key technology to detect network attacks, but improving the accuracy of intrusion detection system is still an urgent problem to be solved. To solve this problem, this paper improves the polynomial regression algorithm and proposes a network intrusion detection model that integrates the improved polynomial regression algorithm and random forest. The model is tested on the NSL-KDD dataset, and the experimental results show that the model improves the detection accuracy and the overall performance is good.
KEYWORDS
Intrusion detection; Polynomial regression; Random ForestCITE THIS PAPER
Jie Zhang, Tao Hong, Xingxiang Lin, Fusion of Improved Polynomial Regression and Random Forest Network Intrusion Detection Model. Advances in Computer, Signals and Systems (2024) Vol. 8: 134-144. DOI: http://dx.doi.org/10.23977/acss.2024.080220.
REFERENCES
[1] Ghorbani A A, Lu W, Tavallaee M. Network intrusion detection and prevention: concepts and techniques[M]. Springer Science & Business Media, 2009.
[2] Ponomarev S, Atkison T. Industrial control system network intrusion detection by telemetry analysis[J]. IEEE Transactions on Dependable and Secure Computing, 2015, 13(2): 252-260.
[3] J.P. Anderson, Computer security threat monitoring and surveillance[R]. Technical report, James P.Anderson Company, Fort Washington, Pennsylvania, 1980.
[4] Liao H J, Lin C H R, Lin Y C, et al. Intrusion detection system: A comprehensive review[J]. Journal of Network and Computer Applications, 2013, 36(1): 16-24.
[5] Jieying Zhou, Pengfei He, Rongfa Qiu, Guo Chen, Weigang Wu. Intrusion Detection Based on Random Forest and Gradient Boosting Tree.Journal of Software, 2021, 32(10):3254-3265
[6] Ji Jun, Jun Li, Chen Chen, et al. Network Intrusion Detection Method Based on Random Forest[J]. Computer Engineering and Applications, 2020, 56(2):7. DOI:10.3778/j.issn.1002-8331.
[7] Hu Zhipeng, Yan Bingyong, Peng Yigong. Cost-sensitive random forest algorithm for hierarchical sampling and its application [J].Computer Engineering and Design, 2019, 40(12):6. DOI:CNKI:SUN:SJSJ.0.2019-12-001.
[8] XIA Jingming, LI Chong, TAN Ling, et al. Improved Network Intrusion Detection Method for Random Forest Classifier [J]. Computer Engineering and Design, 2019, 40(8): 2146-2150.
[9] Shi T, Horvath S. Unsupervised learning with random forest predictors [J]. Journal of Computational and Graphical Statistics, 2006, 15(1): 118-138.
[10] Prasad A M, Iverson L R, Liaw A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction [J]. Ecosystems, 2006, 9: 181-199.
[11] Kwok S W, Carter C. Multiple decision trees[M]//Machine intelligence and pattern recognition. North-Holland, 1990, 9: 327-335.
[12] Ali J, Khan R, Ahmad N, et al. Random forests and decision trees[J]. International Journal of Computer Science Issues (IJCSI), 2012, 9(5): 272.
[13] Kursa M B. Robustness of Random Forest-based gene selection methods[J]. BMC bioinformatics, 2014, 15: 1-8.
[14] M. Tavallaee, E. Bagheri, W. Lu, et al. A detailed analysis of the KDD CUP 99 data set[C]. IEEE International Conference on Computational Intelligence for Security & Defense Applications (CISDA), 2009: 1-6.
[15] Zhang Hongzhuo, Li Zhihua, Wu Pengwei. Research on Intrusion detection technology for high-dimensional unbalanced data [J]. Network Security Technology and Application, 2023(06):61-63.
[16] van der Gaag M, Hoffman T, Remijsen M, et al. The five-factor model of the Positive and Negative Syndrome Scale II: a ten-fold cross-validation of a revised model [J]. Schizophrenia research, 2006, 85(1-3): 280-287.
[17] Parsaei M R, Rostami S M, Javidan R. A hybrid data mining approach for intrusion detection on imbalanced NSL-KDD dataset [J]. International Journal of Advanced Computer Science and Applications, 2016, 7(6): 20-25.
[18] Yu P S, Yang T C, Chen S Y, et al. Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting [J]. Journal of hydrology, 2017, 552: 92-104.
[19] Faruk D Ö. A hybrid neural network and ARIMA model for water quality time series prediction [J]. Engineering applications of artificial intelligence, 2010, 23(4): 586-594.
[20] Shi M, Hu W, Li M, et al. Ensemble regression based on polynomial regression-based decision tree and its application in the in-situ data of tunnel boring machine[J]. Mechanical Systems and Signal Processing, 2023, 188: 110022.
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