Machine Learning-Based Intrusion Detection: A Comparative Analysis among Datasets and Innovative Feature Reduction for Enhanced Cybersecurity

Authors

  • T. R. Ramesh Computer Applications, SRM Institute of Science and Technology, Tiruchirappalli Campus,
  • T. Jackulin Associate Professor, Department of Computer Science and Engineering, Panimalar Engineering College, Chennai
  • R. Ashok Kumar Assistant Professor, Artificial Intelligence Department, Madanapalle Institute of Technology & Science, Kadiri Road Angallu Madanapalle, Andhrapradesh, 517325
  • K. Chanthirasekaran Professor, Department of Electronics and Communication Engineering, Saveetha Engineering College, Chennai.
  • M. Bharathiraja Professor, Automobile Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Erode

Keywords:

Intrusion Detection Systems, Cybersecurity, Machine Learning, Performance Metrics, Network Security

Abstract

: In the rapidly establishing digital area, the upward push of cyber threats furnishes growing issues in safeguarding statistics, consequently needing the expansion of strong intrusion detection systems (IDS). This study gives an in-depth analysis of Intrusion Detection Systems (IDS), evaluating its class, commonly applied methodology, and the vital position of datasets inside the assessment process. The exploration spans the incorporation of device learning and deep mastering in IDS, demonstrating the cutting-edge qualities and breakthroughs that boost network security. The exam closes with a radical evaluation of general performance signs, along with precision, recall, F1 score, and accuracy, throughout ten illustrations. These indications offer focused and diffused insights regarding the machine's ability to correctly identify and respond with cyber-assaults. This study gives useful insights to aid cybersecurity specialists in upgrading their intrusion detection strategies for increased resilience towards transforming cyber threats. It covers the vital goal of keeping virtual belongings that companies are presently facing.

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Published

12.01.2024

How to Cite

Ramesh, T. R. ., Jackulin, T. ., Kumar, R. A. ., Chanthirasekaran, K. ., & Bharathiraja, M. . (2024). Machine Learning-Based Intrusion Detection: A Comparative Analysis among Datasets and Innovative Feature Reduction for Enhanced Cybersecurity. International Journal of Intelligent Systems and Applications in Engineering, 12(12s), 200–206. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4505

Issue

Vol. 12 No. 12s (2024)

Section

Research Article

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