Network Intrusion Detection Models based on Naives Bayes and C4.5 Algorithms-Reference-Cited by-同舟云学术

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Network Intrusion Detection Models based on Naives Bayes and C4.5 Algorithms

Published:2022-04-17 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON)
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Author:

Mebawondu Olamatanmi Josephine¹,Popoola Olugbemiga Solomon²,Ayogu Ikechukwu Ignatius³,Ugwu Chukwuemeka Christian⁴,Adetunmbi Adebayo Olusola⁴

Affiliation:

1. Federal University of Technology,Department of Computer Science,Akure,Nigeria

2. Ọsun State College of Education,Department of Computer Science,Ila-Ọrangun,Nigeria

3. Federal University of Technology,Department of Computer Science,Owerri,Nigeria

4. Federal University of Technology,Department of Computer Science,Akurẹ,Nigeria

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9802998/9802999/09803086.pdf?arnumber=9803086

Reference19 articles.

1. Performance evaluation of intrusion detection based on machine learning using Apache Spark

2. Development of a Network Intrusion Detection System Using Neural Network;mebawondu;M Tech Dissertation,2018

3. Deep feature weighting for naive Bayes and its application to text classification

4. Building an efficient intrusion detection system based on feature selection and ensemble classifier;zhou;Comput Netw,2020

5. Optimizing the Classification of Network Intrusion Detection Using Ensembles of Decision Trees Algorithm;mebawondu;International Conference on Information and Communication Technology and Applications Springer,2021

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A federated learning approach to network intrusion detection using residual networks in industrial IoT networks;The Journal of Supercomputing;2024-05-15

2. Introducing the UWF-ZeekDataFall22 Dataset to Classify Attack Tactics from Zeek Conn Logs Using Spark’s Machine Learning in a Big Data Framework;Electronics;2023-12-18

3. Hybrid Intrusion Detection System Using Autoencoders and Snort;ICT with Intelligent Applications;2023

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