An Empirical Evaluation of Deep Learning for Network Anomaly Detection
Abstract
Deep learning has been given a great deal of attention with its success story in many areas such as image analysis and speech recognition. In particular, deep learning is good at dealing with high-dimensional data exhibiting non-linearity. Our preliminary study reveals a very high degree of non-linearity from network traffic data, which explains why it is hard to improve the detection accuracy by using conventional machine learning techniques (e.g., SVM, Random Forest, Ad-aboosting). In this study, we empirically evaluate deep learning to see its feasibility for network anomaly detection. We examine a set of deep learning models constructed based on the Fully Connected Network (FCN), Variational AutoEncoder (VAE), and Long Short-Term Memory with Sequence to Sequence (LSTM Seq2Seq) structures, with two public traffic data sets that have distinctive properties with respect to the distribution of normal and attack populations. Our experimental results confirm the potential of deep learning models for network anomaly detection, and the model based on the LSTM Seq2Seq structure shows a highly promising performance, yielding 99% of binary classification accuracy on the public data sets.
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Certain studies employ specialized evaluation methods tailored to specific problems, such as response time and ROC curve.
“Additionally, certain studies employ specialized evaluation methods tailored to address specific problems, such as response time (e.g.Lu et al. 2020) [151], receiver operating characteristic (ROC) curve (e.g.Zhang et al. 2019) [275] etc.”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [151]
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An Empirical Study on Network Anomaly Detection Using Convolutional Neural Networks
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Self-citation
- Authors
- Malaiya, R. K., Kwon, D., Kim, J., Suh, S. C., Kim, H., & Kim, I.
- Affiliations
- East Texas A&M University (US)
- Electronics and Telecommunications Research Institute (KR)
- Venue
- 2018 International Conference on Computing, Networking and Communications (ICNC)
- Publisher
- IEEE
- Published
- 2018-03
- Pages
- 893-898
- ISSN
- 2325-2626 (ISSN-L)
- OpenAlex
- W2809097945
- Cited by
- 62
- References
- 17
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 3 · 2023: 16 · 2022: 8 · 2021: 8 · 2020: 10 · 2019: 7 · 2018: 2
- FWCI
- 5.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- 2012 International Conference on Computing, Networking and Communications (ICNC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- network anomaly detection, deep learning, network traffic data, nonlinearity, fully connected networks, variational autoencoder, LSTM, sequence-to-sequence models, binary classification, intrusion detection
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