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 Zhang

ABSTRACT

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 Forest

CITE 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.

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