Evidence map›Paper›PMID 35100305›Full record

ArticlePloS one2022

An improved X-means and isolation forest based methodology for network traffic anomaly detection.

Yifan Feng, Weihong Cai, Haoyu Yue, Jianlong Xu, Yan Lin, Jiaxin Chen, Zijun Hu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2022. 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
2.5field-weighted citation impact, top 10% 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, 19 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

7 authors at 1 institution in 1 country.

Yifan FengCollege of Engineering, Shantou University, ShanTou, Guangdong, China.ORCID 0000-0002-6671-4941
Weihong CaiCollege of Engineering, Shantou University, ShanTou, Guangdong, China.
Haoyu YueCollege of Engineering, Shantou University, ShanTou, Guangdong, China.
Jianlong XuCollege of Engineering, Shantou University, ShanTou, Guangdong, China.
Yan LinSangfor Technologies Incorporation, ShenZhen, Guangdong, China.
Jiaxin ChenCollege of Engineering, Shantou University, ShanTou, Guangdong, China.
Zijun HuCollege of Engineering, Shantou University, ShanTou, Guangdong, China.
Shantou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Anomaly detection in network traffic is becoming a challenging task due to the complexity of large-scale networks and the proliferation of various social network applications. In the actual industrial environment, only recently obtained unlabelled data can be used as the training set. The accuracy of the abnormal ratio in the training set as prior knowledge has a great influence on the performance of the commonly used unsupervised algorithms. In this study, an anomaly detection algorithm based on X-means and iForest is proposed, named X-iForest, which clusters the standard Euclidean distance between the abnormal points and the normal cluster centre to achieve secondary filtering by using X-means. We compared X-iForest with seven mainstream unsupervised algorithms in terms of the AUC and anomaly detection rates. A large number of experiments showed that X-iForest has notable advantages over other algorithms and can be well applied to anomaly detection of large-scale network traffic data.

Indexed as

AlgorithmsNeural Networks, ComputerArea Under CurveComputer Simulation

Identifiers

PMID35100305
PMCPMC8803200
OpenAlexW4210695580

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.