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Network intrusion detection based on improved KNN algorithm.

Hongsheng Bao¹, Jie Gao²

¹Internet of Things and Intelligent Engineering School, Jiangsu Vocational Institute of Commerce, Nanjing, 211168, China.

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|August 14, 2025

Summary

A new K-nearest neighbor algorithm improves network attack detection, classifying normal and probing threats with over 85% accuracy. This cybersecurity enhancement significantly boosts overall attack detection rates.

Keywords:

Classification Incremental learning KNN algorithm Network intrusion Three branch decision-making

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Cybersecurity
Machine Learning
Network Intrusion Detection

Background:

Increasing frequency of network attacks like Trojan horses, worms, and ransomware pose significant threats to cybersecurity and national security.
Traditional methods may be insufficient to address the evolving landscape of cyber threats.

Purpose of the Study:

To propose a novel three-branch decision soft increment K-nearest neighbor algorithm for enhanced network attack classification.
To improve the accuracy and efficiency of detecting various types of cyberattacks.

Main Methods:

Developed a three-branch decision soft increment K-nearest neighbor algorithm representing class clusters as interval sets.
Introduced an initial K-nearest neighbor algorithm based on representative points for pre-clustering datasets.
Implemented pre-clustering to mitigate data processing order influence on results.

Main Results:

The improved K-nearest neighbor algorithm achieved over 85% accuracy in classifying Normal, DoS, and Probing attacks.
Achieved an average accuracy of 57.32% for U2R attacks.
The proposed method demonstrated the highest classification accuracy on the dataset, with a detection rate exceeding 98% for all attack types.

Conclusions:

The proposed algorithm effectively classifies common network attacks with high accuracy.
The method shows significant promise for enhancing overall network security and threat detection capabilities.
Further research may focus on improving U2R attack detection rates.