Anomaly Detection Using Feature Selection to Identify Security Threats in Network Traffic

IJCNIS is a peer-reviewed international journal focusing on theories, methods, and applications in networks and information security, including emerging trends in IoT, blockchain, and optical communication.

Publication Details

Published
2025-02-01
DOI
https://doi.org/10.48047/ijcnis.17.2.1-17
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

Anomaly Detection Using Feature Selection to Identify Security Threats in Network Traffic

2 citations
Network Security and Intrusion Detection
2.97
article

Anomaly Detection Using Feature Selection to Identify Security Threats in Network Traffic

article en
2 citations

Abstract

IJCNIS is a peer-reviewed international journal focusing on theories, methods, and applications in networks and information security, including emerging trends in IoT, blockchain, and optical communication.

Vol. 17(2)
Openalex Percentile: Top 10%
Network Security and Intrusion Detection
2.97
DOISourceOpenAlex
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.

Anomaly Detection Using Feature Selection to Identify Security Threats in Network Traffic · (2025) | TGRS Research Map | TGRS