SA3C-ID: a novel network intrusion detection model using feature selection and adversarial training Article Swipe
Wanwei Huang
,
Haobin Tian
,
Lei Wang
,
Sun’an Wang
,
Kun Wang
,
Songze Li
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.7717/peerj-cs.3089
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.7717/peerj-cs.3089
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 via one-hot encoding and standardization. Subsequently, the refined data undergoes feature selection employing an improved pigeon-inspired optimizer (PIO) algorithm. This operation eliminates redundant and irrelevant features, consequently reducing data dimensionality while maintaining critical information. Next, the network intrusion detection process is modeled as a Markov decision process and integrated with the Soft Actor-Critic (SAC) reinforcement learning algorithm, with a view to constructing agents; In the context of adversarial training, two agents, designated as the attacker and the defender, are defined to perform asynchronous adversarial training. During this training process, both agents calculate the reward value, update their respective strategies, and transfer parameters based on the classification results. Finally, to verify the robustness and generalization ability of the SA3C-ID model, ablation experiments and comparative evaluations are conducted on two benchmark datasets, NSL-KDD and CSE-CIC-IDS2018. The experimental results demonstrate that SA3C-ID exhibits superior performance in comparison to other prevalent intrusion detection models. The F1-score attained by SA3C-ID was 92.58% and 98.76% on the NSL-KDD and CSE-CIC-IDS2018 datasets, respectively.
Related Topics
Concepts
Adversarial system
Feature selection
Computer science
Training (meteorology)
Selection (genetic algorithm)
Intrusion detection system
Artificial intelligence
Feature (linguistics)
Intrusion
Machine learning
Pattern recognition (psychology)
Geology
Geography
Philosophy
Meteorology
Linguistics
Geochemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7717/peerj-cs.3089
- OA Status
- gold
- Cited By
- 1
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412809533
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412809533Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.7717/peerj-cs.3089Digital Object Identifier
- Title
-
SA3C-ID: a novel network intrusion detection model using feature selection and adversarial trainingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-07-31Full publication date if available
- Authors
-
Wanwei Huang, Haobin Tian, Lei Wang, Sun’an Wang, Kun Wang, Songze LiList of authors in order
- Landing page
-
https://doi.org/10.7717/peerj-cs.3089Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.7717/peerj-cs.3089Direct OA link when available
- Concepts
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Adversarial system, Feature selection, Computer science, Training (meteorology), Selection (genetic algorithm), Intrusion detection system, Artificial intelligence, Feature (linguistics), Intrusion, Machine learning, Pattern recognition (psychology), Geology, Geography, Philosophy, Meteorology, Linguistics, GeochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
-
35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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