Improved hybrid algorithm for detection of intrusion into computer networks

Subject Areas : New technologies in distributed systems and algorithmic computing

Safanaz Heidari ¹ , Firozeh Razavi ^{2 *}

1 - 2Department od Computer, Miandoab Branch, Islamic Azad University, miandoab, Iran,
2 - Department of Information Technology Management, Raja University, Qazvin, Iran

Received: 2024-06-20 Accepted : 2025-05-16 Published : 2025-06-07

Keywords: Intrusion detection system, Attack detection, Strong gradient boosting, Clustering techniques, Machine learning,

Abstract :

Identifying malicious networks has been a subject of study for decades, and since the volume of network traffic is increasing day by day, there is a need for a successful intrusion-detection system that can make the identification process easier during attacks. The aim behind this research was to take decisions more accurately via real time and faster processing. The purpose of this research was to detect intrusion into computer networks by combining K-means and XG-boost clustering algorithms. The proposed method was performed in two stages. In the first stage, the pre-processing was done by normalizing and digitizing the data set, as well as removing outliers based on two PCA methods and reducing the dimensions of the feature, then using the learner. The researchers used the k-means algorithm to find the optimal number of clusters, finally the Elbow method was utilized to find the optimum number of clusters. The second stage consisted of classifying malicious and normal network traffic from each other by combining K-means and XG-Boost algorithms on computing platforms. The experiments in this article were done using the NSLKDD data set and its implementation in the knime emulator platform; the final evaluation results revealed the superiority of the error detection rate, and the accuracy and correctness of the proposed algorithm compared over other similar methods.

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