An Online Network Intrusion Detection Model Based on Improved Regularized Extreme Learning Machine
Extreme learning machine (ELM) is a novel single-hidden layer feedforward neural network to obtain fast learning speed by randomly initializing weights and deviations. Due to its extremely fast learning speed, it has been widely used in training of massive data in recent years. In order to adapt to...
Saved in:
Main Authors: | Yanqiang Tang, Chenghai Li |
---|---|
Format: | article |
Language: | EN |
Published: |
IEEE
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/c609ef3cb6e14ed38b13afbbd18d4aee |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Non-intrusive load identification based on CF-MF-SE joint feature
by: Guoqing AN, et al.
Published: (2021) -
Comparative Performance Evaluation of Intrusion Detection Based on Machine Learning in In-Vehicle Controller Area Network Bus
by: Tarek Moulahi, et al.
Published: (2021) -
Research Trends in Network-Based Intrusion Detection Systems: A Review
by: Satish Kumar, et al.
Published: (2021) -
Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal
by: Yan Shen, et al.
Published: (2021) -
An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks
by: Georgios Zachos, et al.
Published: (2021)