The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Intrusion detection systems using long short-term memory (LSTM)
Open accessAbstract
An intrusion detection system (IDS) is a device or software application that monitors a network for malicious activity or policy violations. It scans a network or a system for a harmful activity or security breaching. IDS protects networks (Network-based intrusion detection system NIDS) or hosts (Host-based intrusion detection system HIDS), and work by either looking for signatures of known attacks or deviations from normal activity. Deep learning algorithms proved their effectiveness in intrusion detection compared to other machine learning methods. In this paper, we implemented deep learning solutions for detecting attacks based on Long Short-Term Memory (LSTM). PCA (principal component analysis) and Mutual information (MI) are used as dimensionality reduction and feature selection techniques. Our approach was tested on a benchmark data set, KDD99, and the experimental outcomes show that models based on PCA achieve the best accuracy for training and testing, in both binary and multiclass classification.
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A Deep Learning Approach for Network Intrusion Detection System
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High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
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Information Theory and Statistics
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Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection
Kim, J., Kim, J., Thi Thu, H. L., & Kim, H. (2016) · 2016 International Conference on Platform Technology and Service (PlatCon) · doi:10.1109/platcon.2016.7456805 · Cited by 690
Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security
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BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset
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TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System
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A deep learning method with wrapper based feature extraction for wireless intrusion detection system
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Cited by
Design and Development of RNN Anomaly Detection Model for IoT Networks
Ullah, I., & Mahmoud, Q. H. (2022) · IEEE Access · doi:10.1109/access.2022.3176317 · Cited by 270
Metadata
- Authors
- Laghrissi, F., Douzi, S., Douzi, K., & Hssina, B.
- Venue
- Journal of Big Data
- Published
- 2021-05-07
- Volume
- 8
- Issue
- 1
- ISSN
- 2196-1115 (ISSN-L)
- OpenAlex
- W3164999323
- Cited by
- 304
- References
- 22
- FWCI
- 18.54
- Type
- journal article
- Language
- en
- Open access
- gold
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