The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Network Anomaly Detection Using LSTM Based Autoencoder
Open accessAbstract
Anomaly detection aims to discover patterns in data that do not conform to the expected normal behaviour. One of the significant issues for anomaly detection techniques is the availability of labeled data for training/validation of models. In this paper, we proposed a hyper approach based on Long Short Term Memory (LSTM) autoencoder and One-class Support Vector Machine (OC-SVM) to detect anomalies based attacks in an unbalanced dataset, by training the models using only examples of normal classes. The LSTM-autoencoder is trained to learn the normal traffic pattern and to learn the compressed representation of the input data (i.e. latent features) and then feed it to an OC-SVM approach. The hybrid model overcomes the shortcomings of the separate OC-SVM, in which its low capability to operate with massive and high-dimensional datasets. Additionally, we perform our experiments using the most recent dataset (InSDN) of Intrusion Detection Systems (IDSs) for SDN environments. The experimental results show that the proposed model provides higher detection rate and reduces the processing time significantly. Hence, our method provides great confidence in securing SDN networks from malicious traffic.
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Cites
Reducing the dimensionality of data with neural networks
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Gers, F. A. (1999) · 9th International Conference on Artificial Neural Networks: ICANN '99 · doi:10.1049/cp:19991218 · Cited by 2,337
A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
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Support Vector Method for Novelty Detection
Schölkopf, B., Williamson, R. C., Smola, A. J., Shawe-Taylor, J. S., & Platt, J. C. (1999) · Cited by 2,040
Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction
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Deep Learning for Anomaly Detection: A Survey
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Deep learning approach for Network Intrusion Detection in Software Defined Networking
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A survey of deep learning-based network anomaly detection
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Cited by
DDoS attack detection and mitigation using deep neural network in SDN environment
Hnamte, V., Najar, A. A., Nhung-Nguyen, H., Hussain, J., & Sugali, M. N. (2024) · Computers & Security · doi:10.1016/j.cose.2023.103661 · Cited by 164
Aero: Automotive ethernet real-time observer for anomaly detection in in-vehicle networks
Jeong, S., Kim, H. K., Han, M. L., & Kwak, B. I. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3324949 · Cited by 31
Metadata
- Authors
- Said Elsayed, M., Le-Khac, N.-A., Dev, S., & Jurcut, A. D.
- Venue
- Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks
- Publisher
- ACM
- Published
- 2020-11-16
- Pages
- 37-45
- OpenAlex
- W3109037541
- Cited by
- 285
- References
- 23
- FWCI
- 22.34
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Said Elsayed, M., Le-Khac, N.-A., Dev, S., & Jurcut, A. D. (2020). Network Anomaly Detection Using LSTM Based Autoencoder. Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks. https://doi.org/10.1145/3416013.3426457
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