Evidence map›Paper›PMID 36908758›Full record

ArticleFrontiers in computational neuroscience2023

Kohonen neural network and symbiotic-organism search algorithm for intrusion detection of network viruses.

Guo Zhou, Fahui Miao, Zhonghua Tang, Yongquan Zhou, Qifang Luo

Open access · goldAbstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.4field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 7 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Guo ZhouDepartment of Science and Technology Teaching, China University of Political Science and Law, Beijing, China.
Fahui MiaoCollege of Artificial Intelligence, Guangxi University for Nationalities, Nanning, China.
Zhonghua TangCollege of Artificial Intelligence, Guangxi University for Nationalities, Nanning, China.
Yongquan ZhouCollege of Artificial Intelligence, Guangxi University for Nationalities, Nanning, China.
Qifang LuoDepartment of Science and Technology Teaching, China University of Political Science and Law, Beijing, China.
Guangxi University for Nationalities · CNChina University of Political Science and Law · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The development of the Internet has made life much more convenient, but forms of network intrusion have become increasingly diversified and the threats to network security are becoming much more serious. Therefore, research into intrusion detection has become very important for network security. Methods: In this paper, a clustering algorithm based on the symbiotic-organism search (SOS) algorithm and a Kohonen neural network is proposed. Results: The clustering accuracy of the Kohonen neural network is improved by using the SOS algorithm to optimize the weights in the Kohonen neural network. Discussion: Our approach was verified with the KDDCUP99 network intrusion data. The experimental results show that SOS-Kohonen can effectively detect intrusion. The detection rate was higher, and the false alarm rate was lower.

Indexed as

detection ratefalse alarm rateintrusion detectionKohonen neural networksymbiotic-organism search algorithm

Identifiers

PMID36908758
PMCPMC9992525
OpenAlexW4321497758

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.