Work

Network Anomaly Detection

Year: 2025

Type: article

Abstract: This study explores the efficiency of genetic algorithms for feature selection in network anomaly detection systems, combined with machine learning techniques to enhance detection accuracy. The focus is on identifying optimal feature subsets contributing to high classification accuracy in a network environment dataset. The findings demonstrate significant improvements in detection rates, emphasizing the potential of integrating genetic algorithms with machine learning for network security applications.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15412033

Authors: Mehjabin Hossain, Md. Imtiaz Hasan

Institution: American International University-Bangladesh

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6893254928

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