The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Anomaly-Based Intrusion Detection From Network Flow Features Using Variational Autoencoder
Open accessAbstract
The rapid increase in network traffic has recently led to the importance of flow-based intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly-based methods, which can identify unknown attacks are also integrated into these systems. In this study, the focus is concentrated on the detection of anomalous network traffic (or intrusions) from flow-based data using unsupervised deep learning methods with semi-supervised learning approach. More specifically, Autoencoder and Variational Autoencoder methods were employed to identify unknown attacks using flow features. In the experiments carried out, the flow-based features extracted out of network traffic data, including typical and different types of attacks, were used. The Receiver Operating Characteristics (ROC) and the area under ROC curve, resulting from these methods were calculated and compared with One-Class Support Vector Machine. The ROC curves were examined in detail to analyze the performance of the methods in various threshold values. The experimental results show that Variational Autoencoder performs, for the most part, better than Autoencoder and One-Class Support Vector Machine.
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LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
(2010) · Cited by 14,134
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992
Estimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
Fast Training of Support Vector Machines Using Sequential Minimal Optimization
Platt, J. C. (1998) · Advances in Kernel Methods · doi:10.7551/mitpress/1130.003.0016 · Cited by 5,437
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018) · Proceedings of the 4th International Conference on Information Systems Security and Privacy · doi:10.5220/0006639801080116 · Cited by 5,318 · cited 1×
“CICIDS2017 [61].”
Moustafa, N., & Slay, J. (2015) · 2015 Military Communications and Information Systems Conference (MilCIS) · doi:10.1109/milcis.2015.7348942 · Cited by 4,033
(2013) · Cited by 3,740
Classification assessment methods
Tharwat, A. (2021) · Applied Computing and Informatics · doi:10.1016/j.aci.2018.08.003 · Cited by 2,417
Cited by
Machine-Learning-Based Darknet Traffic Detection System for IoT Applications
Abu Al-Haija, Q., Krichen, M., & Abu Elhaija, W. (2022) · Electronics · doi:10.3390/electronics11040556 · Cited by 120
One-Class Anomaly Detection for Industrial Applications: A Comparative Survey and Experimental Study
Paolini, D., Dini, P., Soldaini, E., & Saponara, S. (2025) · Computers · doi:10.3390/computers14070281 · Cited by 21
Shafi, M., Lashkari, A. H., & Roudsari, A. H. (2025) · Journal of Network and Systems Management · doi:10.1007/s10922-025-09917-0 · Cited by 10
Variational Autoencoders (VAEs) for Anomaly Detection
Tahir, S. (2024) · Advances in Digital Crime, Forensics, and Cyber Terrorism · doi:10.4018/979-8-3693-8944-7.ch009 · Cited by 4
Metadata
- Authors
- Zavrak, S., & Iskefiyeli, M.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 108346-108358
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3035311645
- Cited by
- 322
- References
- 58
- FWCI
- 19.33
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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