Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network
Open accessAbstract
Intrusion detection system (IDS) plays an important role in network security by discovering and preventing malicious activities. Due to the complex and time-varying network environment, the network intrusion samples are submerged into a large number of normal samples, which leads to insufficient samples for model training and detection results with a high false detection rate. According to the problem of data imbalance, we propose a network intrusion detection algorithm combined hybrid sampling with deep hierarchical network. Firstly, we use the one-side selection (OSS) to reduce the noise samples in majority category, and then increase the minority samples by Synthetic Minority Over-sampling Technique (SMOTE). In this way, a balanced dataset can be established to make the model fully learn the features of minority samples and greatly reduce the model training time. Secondly, we use convolution neural network (CNN) to extract spatial features and Bi-directional long short-term memory (BiLSTM) to extract temporal features, which forms a deep hierarchical network model. The proposed network intrusion detection algorithm was verified by experiments on the NSL-KDD and UNSW-NB15 dataset, and the classification accuracy can achieve 83.58% and 77.16%, respectively.
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Jabbar and his team proposed an ensemble classifier that combines Random Forest and Average One-Dependence Estimator (AODE) algorithms to tackle the problem of attribute dependence in Naive Bayes, and the use of Random Forest improves accuracy and decreases false positive results.
“Jabbar and his team proposed an ensemble classifier that combines Random Forest and Average One-Dependence Estimator (AODE) algorithms to tackle the problem of attribute dependence in Naive Bayes.The use of Random Forest (RF) improves accuracy and decreases false positive results( [112].”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [112]
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- Authors
- Jiang, K., Wang, W., Wang, A., & Wu, H.
- Affiliations
- Harbin University of Science and Technology (CN)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 32464-32476
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3005805195
- Cited by
- 446
- References
- 31
- Citations by year
- 2026: 30 · 2025: 70 · 2024: 102 · 2023: 119 · 2022: 73 · 2021: 42 · 2020: 9 · 2018: 1
- FWCI
- 28.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (NSFC-61671190)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
- Keywords
- network intrusion detection, class imbalance, hybrid sampling, SMOTE, convolutional neural networks, bidirectional LSTM, temporal feature extraction, noise reduction, false detection rate
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