Automated Detection Of Network Intrusions Using Machine Learning in Real-Time Systems

Aulia Novi; Ryan Satria

doi:10.62951/ijcts.v1i2.63

Authors

Aulia Novi Universitas Hasanuddin
Ryan Satria Universitas Hasanuddin

DOI:

https://doi.org/10.62951/ijcts.v1i2.63

Keywords:

Intrusion detection, Real-time systems, Machine learning, Support vector machine, Network security, Decision tree

Abstract

Network intrusion detection is crucial for maintaining the integrity of real-time systems. This paper evaluates various machine learning algorithms, including support vector machines (SVM) and decision trees, for real-time intrusion detection. Through extensive testing on simulated datasets, the study highlights the advantages of automated detection in reducing response times and enhancing network security.

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