ESVI-GaMM: A fast network intrusion detection approach based on the Bayesian gamma mixture model-Reference-Cited by-同舟云学术

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ESVI-GaMM: A fast network intrusion detection approach based on the Bayesian gamma mixture model

Published:2024-09 Issue: Volume:678 Page:121001
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

He Wenda,Cai Xiangrui^ORCID,Lai Yuping,Yuan Xiaojie

Funder

Beijing University of Posts and Telecommunications

National Key Research and Development Program of China

Natural Science Foundation of Tianjin Municipality

Nankai University

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference49 articles.

1. Challenges and novel solutions for 5g network security, privacy and trust;Mazurczyk;IEEE Wirel. Commun.,2020

2. Security attacks in opportunistic mobile networks: a systematic literature review;Altaweel;J. Netw. Comput. Appl.,2024

3. A systematic literature review of methods and datasets for anomaly-based network intrusion detection;Yang;Comput. Secur.,2022

4. Zerowall: detecting zero-day web attacks through encoder-decoder recurrent neural networks;Tang,2020

5. Novel geometric area analysis technique for anomaly detection using trapezoidal area estimation on large-scale networks;Moustafa;IEEE Trans. Big Data,2017

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