Evidence map›Paper›PMID 40989373›Full record

ArticlePeerJ. Computer science2025

SA3C-ID: a novel network intrusion detection model using feature selection and adversarial training.

Wanwei Huang, Haobin Tian, Lei Wang, Sunan Wang, Kun Wang, Songze Li

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Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

6 authors.

Wanwei HuangCollege of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China.
Haobin TianCollege of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China.
Lei WangChina Research Institute of Radio Wave Propagation, Xinxiang, Henan, China.
Sunan WangShenzhen Polytechnic University, Shenzhen, Guangdong, China.
Kun WangZhengzhou Xinda Institute of Advanced Technology, Zhengzhou, Henan, China.
Songze LiHenan Xindawangyu Science & Technology Co., Ltd., Zhengzhou, Henan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the continuous proliferation of emerging technologies such as cloud computing, 5G networks, and the Internet of Things, the field of cybersecurity is facing an increasing number of complex challenges. Network intrusion detection systems, as a fundamental part of network security, have become increasingly significant. However, traditional intrusion detection methods exhibit several limitations, including insufficient feature extraction from network data, high model complexity, and data imbalance, which result in issues like low detection efficiency, as well as frequent false positives and missed alarms. To address the above issues, this article proposed an adversarial intrusion detection model (Soft Adversarial Asynchronous Actor-Critic Intrusion Detection, SA3C-ID) based on reinforcement learning. Firstly, the raw dataset is preprocessed

Indexed as

Adversarial trainingCybersecurityFeature selectionNetwork intrusion detection systemReinforcement learning

Identifiers

PMID40989373
PMCPMC12453758

What OpenQuestion holds

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Registered trials

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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.