Intrusion Detection Model on Network Data with Deep Adaptive Multi-Layer Attention Network (DAMLAN)

The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems (IDS) for enhanced network security. While conventional IDSs can be unsuitable for detecting different and emerging attacks... | Find, read and cite all the research you need on Tech Science Press

Authors

Publication Details

Journal
Computer Modeling in Engineering & Sciences
Published
2025-01-01
DOI
https://doi.org/10.32604/cmes.2025.065188
Citations
2
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
2.97
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Intrusion Detection Model on Network Data with Deep Adaptive Multi-Layer Attention Network (DAMLAN)

Fatma S. Alrayes, Syed Umar Amin, Nada Ali Hakami, Mohammed K. Alzaylaee et al.
2 citations
Computer Modeling in Engineering & Sciences
Network Security and Intrusion Detection
2.97
article

Intrusion Detection Model on Network Data with Deep Adaptive Multi-Layer Attention Network (DAMLAN)

Fatma S. Alrayes, Syed Umar Amin, Nada Ali Hakami, Mohammed K. Alzaylaee, Tariq Kashmeery
article en
2 citations

Abstract

The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems (IDS) for enhanced network security. While conventional IDSs can be unsuitable for detecting different and emerging attacks... | Find, read and cite all the research you need on Tech Science Press

Computer Modeling in Engineering & SciencesVol. 144(1)
Openalex Percentile: Top 8%
Network Security and Intrusion Detection
2.97
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.