ArticleScientific reports2022
ID-RDRL: a deep reinforcement learning-based feature selection intrusion detection model.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 72 citations in OpenAlex.
- Application of AI in Cyberattack Detection: A Review.Sensors (Basel, Switzerland) · 2026Review
- MultiNet-IDS: an ensemble of DRL and LSTM for improved intrusion detection in wireless sensor networks.Scientific reports · 2025Article
- Bi directional sparse attention recurrent autoencoder based intrusion detection for VANET security with tuna swarm optimization.Scientific reports · 2025Article
- SA3C-ID: a novel network intrusion detection model using feature selection and adversarial training.PeerJ. Computer science · 2025Article
- Age-appropriate design of smart senior care product APP interface based on deep learning.Heliyon · 2024Article
- A holistic and proactive approach to forecasting cyber threats.Scientific reports · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Network assaults pose significant security concerns to network services; hence, new technical solutions must be used to enhance the efficacy of intrusion detection systems. Existing approaches pay insufficient attention to data preparation and inadequately identify unknown network threats. This paper presents a network intrusion detection model (ID-RDRL) based on RFE feature extraction and deep reinforcement learning. ID-RDRL filters the optimum subset of features using the RFE feature selection technique, feeds them into a neural network to extract feature information and then trains a classifier using DRL to recognize network intrusions. We utilized CSE-CIC-IDS2018 as a dataset and conducted tests to evaluate the model's performance, which is comprised of a comprehensive collection of actual network traffic. The experimental results demonstrate that the proposed ID-RDRL model can select the optimal subset of features, remove approximately 80% of redundant features, and learn the selected features through DRL to enhance the IDS performance for network attack identification. In a complicated network environment, it has promising application potential in IDS.
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