Network Traffic Intrusion Detection

The paper explores the application of machine learning algorithms for network traffic intrusion detection with the aim of enhancing the security of information systems. More specifically, it provides a comprehensive insight into the current state of the art in intrusion detection, and gives a review...

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Bibliographic Details
Published in2024 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) pp. 1 - 6
Main Authors Zada, Frane, Antonic, Martina
Format Conference Proceeding
LanguageEnglish
Published University of Split, FESB 26.09.2024
Subjects
Accuracy
Decision trees
Information systems
information systems security
Intrusion detection
Machine learning
Network security
network traffic
Reviews
Software
Telecommunication traffic
Telecommunications
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Summary:The paper explores the application of machine learning algorithms for network traffic intrusion detection with the aim of enhancing the security of information systems. More specifically, it provides a comprehensive insight into the current state of the art in intrusion detection, and gives a review of relevant literature, research methodology, implementation of various machine learning models, and analysis of the obtained results. Our findings indicate the outstanding potential of models, such as decision trees, in identifying malicious activities, offering valuable guidance for the development of more efficient systems for network security.
ISSN:1847-358X
DOI:10.23919/SoftCOM62040.2024.10721884