Clustering-based label estimation for network anomaly detection
A substantial body of work has been done to identify network anomalies using supervised and unsupervised learning techniques with their unique strengths and weaknesses. In this work, we propose a new approach that takes advantage of both worlds of unsupervised and supervised learnings. The main obje...
Main Authors: | Sunhee Baek, Donghwoon Kwon, Sang C. Suh, Hyunjoo Kim, Ikkyun Kim, Jinoh Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
KeAi Communications Co., Ltd.
2021-02-01
|
Series: | Digital Communications and Networks |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864818301779 |
Similar Items
-
An Empirical Evaluation of Deep Learning for Network Anomaly Detection
by: Ritesh K. Malaiya, et al.
Published: (2019-01-01) -
Detecting Anomalies in Time Series Using Kernel Density Approaches
by: Robin Frehner, et al.
Published: (2024-01-01) -
Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study
by: Marcio Trindade Guerreiro, et al.
Published: (2021-10-01) -
Detection of Cluster Anomalies With ML Techniques
by: Joanna Kosinska, et al.
Published: (2022-01-01) -
Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering
by: Young Jong Song, et al.
Published: (2023-06-01)