An Efficient ML-Based Model for Network Intrusion Detection System

Priyanshu Sinha, Shiv Prakash, Sudhanshu Kumar Jha, Vandana Rathore, Tiansheng Yang, Rajkumar Singh Rathore, Abhishek Singh, Rahul Mishra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the modern era, cyber-security is a solution to protect systems, networks, and programs from different attacks. A network intrusion detection system (NIDS) is a secure system that analyses vulnerabilities and security attacks in cyberspace which is used to find malicious activity. Machine Learning (ML) techniques are frequently used to solve anomaly-related problems. Therefore, a data-driven machine learning model is proposed to detect the issues related to NIDS through the benchmark dataset. The study's results depict that the proposed model outperforms the contemporary model.
Original languageEnglish
Title of host publication2024 International Conference on Decision Aid Sciences and Applications (DASA)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-5
Number of pages5
ISBN (Electronic)9798350369106
ISBN (Print)979-8-3503-6911-3
DOIs
Publication statusPublished - 17 Jan 2025
Event2024 International Conference on Decision Aid Sciences and Applications (DASA) - Manama, Bahrain
Duration: 11 Dec 202412 Dec 2024

Publication series

Name2024 International Conference on Decision Aid Sciences and Applications (DASA)
PublisherIEEE Computer Society

Conference

Conference2024 International Conference on Decision Aid Sciences and Applications (DASA)
Country/TerritoryBahrain
CityManama
Period11/12/2412/12/24

Keywords

  • Applications
  • Machine Learning (ML)
  • NIDS
  • SMOTE
  • WSN Security
  • Wireless Sensor Networks (WSN)

Cite this