An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System-Reference-Cited by-同舟云学术

An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System

Published:2022 Issue: Volume:10 Page:46424-46441
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Shitharth S.¹^ORCID,Kshirsagar Pravin R.²^ORCID,Balachandran Praveen Kumar³^ORCID,Alyoubi Khaled H.⁴,Khadidos Alaa O.⁴^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Kebri Dehar University, Kebri Dehar, Somali, Ethiopia

2. Department of Artificial Intelligence, G. H. Raisoni College of Engineering, Nagpur, Maharashtra, India

3. Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Hyderabad, Telangana, India

4. Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09765958.pdf?arnumber=9765958

Reference60 articles.

1. An Adaptive Ensemble Machine Learning Model for Intrusion Detection

2. Building an Effective Intrusion Detection System by Using Hybrid Data Optimization Based on Machine Learning Algorithms

3. Feature Selection Based Hybrid Anomaly Intrusion Detection System Using K Means and RBF Kernel Function

4. A framework for designing resilient distributed intrusion detection systems for critical infrastructures

5. Flow Correlation Degree Optimization Driven Random Forest for Detecting DDoS Attacks in Cloud Computing

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