Rajavenkatesswaran KC¹*, Ari Bharathi A², Gayathri R², Mathan C² and Vikram B²

¹Assistant Professor, Department of Information Technology, Nandha College of Technology, India
²UG-Final Year, Department of Information Technology, Nandha College of Technology, India

*Corresponding Author: Rajavenkatesswaran KC, Assistant Professor, Department of Information Technology, Nandha College of Technology, India.

Received: May 09, 2024; Published: June 11, 2024

Abstract

Malicious behavior on computer networks is monitored and detected by intrusion detection systems, or IDSs. Rule-based systems, which might be hard to maintain and might not be able to identify new attack vectors, are often used by traditional IDSs. IDSs are using machine learning (ML) methods more often since they can learn from data to identify new threats and increase their accuracy over time. To improve network security, the suggested intrusion detection system combines several machine learning approaches with the Random Forest algorithm. When categorizing complicated data, the Random Forests (RF) method yields excellent accuracy results. An established intrusion detection benchmark, the KDD Cup dataset, is used to assess the system's performance. In an ever-changing threat environment, this technique shows tremendous promise in detecting and mitigating harmful actions inside computer networks, improving cybersecurity.

Keywords: Intrusion Detection; Feature Selection; Machine Learning; Random Forest; Detection Rate

References

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Citation

Citation: Rajavenkatesswaran KC., et al. “A Random Forest Approach and Principal Component Analysis in Intrusion Detection System Using Machine Learning".Acta Scientific Computer Sciences 6.6 (2024): 28-31.

Copyright

Copyright: © 2024 Rajavenkatesswaran KC., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.