Multi-Head Attention Enhanced Parallel Dilated Convolution and Residual Learning for Network Traffic Anomaly Detection

Abnormal network traffic, as a frequent security risk, requires a series of techniques to categorize and detect it. Existing network traffic anomaly detection still faces challenges: the inability to fully extract local and g... | Find, read and cite all the research you need on Tech Science Press

Authors

Jian Mao (ORCID: https://orcid.org/0000-0003-1345-7408)
Jingquan Lin (ORCID: https://orcid.org/0000-0002-6602-3489)
Z. Y. You (ORCID: https://orcid.org/0009-0002-8645-0899)
Guorong Qi
Kai Huang

Publication Details

Journal: Computers, materials & continua/Computers, materials & continua (Print)
Published: 2024-12-11
DOI: https://doi.org/10.32604/cmc.2024.058396
Citations: 3
Primary Topic: Anomaly Detection Techniques and Applications
Type: article
Field-Weighted Citation Impact: 1.04

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article

Multi-Head Attention Enhanced Parallel Dilated Convolution and Residual Learning for Network Traffic Anomaly Detection

Jian Mao, Jingquan Lin, Z. Y. You, Guorong Qi et al.

Dec 11, 2024

3 citations

Computers, materials & continua/Computers, materials & continua (Print)

Anomaly Detection Techniques and Applications

1.04

article

Multi-Head Attention Enhanced Parallel Dilated Convolution and Residual Learning for Network Traffic Anomaly Detection

Jian Mao, Jingquan Lin, Z. Y. You, Guorong Qi, Kai Huang

December 11, 2024article en

3 citations

Abstract

Abnormal network traffic, as a frequent security risk, requires a series of techniques to categorize and detect it. Existing network traffic anomaly detection still faces challenges: the inability to fully extract local and g... | Find, read and cite all the research you need on Tech Science Press

Computers, materials & continua/Computers, materials & continua (Print)Vol. 82(2)

Openalex Percentile: Top 18%

Anomaly Detection Techniques and Applications

1.04

DOI Open Access PDF OpenAlex

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