An Improved Convolutional Neural Network Model for Intrusion Detection in Networks
Abstract
Network intrusion detection is an important component of network security. Currently, the popular detection technology used the traditional machine learning algorithms to train the intrusion samples, so as to obtain the intrusion detection model. However, these algorithms have the disadvantage of low detection rate. Deep learning is more advanced technology that automatically extracts features from samples. In view of the fact that the accuracy of intrusion detection is not high in traditional machine learning technology, this paper proposes a network intrusion detection model based on convolutional neural network algorithm. The model can automatically extract the effective features of intrusion samples, so that the intrusion samples can be accurately classified. Experimental results on KDD99 datasets show that the proposed model can greatly improve the accuracy of intrusion detection.
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FL reduces data security and privacy concerns by maintaining local data stores, unlike centralized ML which requires datasets to reside on a single server.
“In contrast to traditional centralized ML methods, which require datasets to reside on a single server, FL minimizes security and privacy concerns by keeping local data stores [12, 13].”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [12]
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Dew-Cloud-Based Hierarchical Federated Learning for Intrusion Detection in IoMT
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- Authors
- Khan, R. U., Zhang, X., Alazab, M., & Kumar, R.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Charles Darwin University (AU)
- Venue
- 2019 Cybersecurity and Cyberforensics Conference (CCC)
- Publisher
- IEEE
- Published
- 2019-05
- Pages
- 74-77
- OpenAlex
- W2918854598
- Cited by
- 141
- References
- 15
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 20 · 2023: 30 · 2022: 34 · 2021: 20 · 2020: 15 · 2019: 1
- FWCI
- 15.13
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Cybersecurity and Cyberforensics Conference (CCC) (publishedVersion)
- 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 › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- network intrusion detection, convolutional neural networks, automatic feature extraction, intrusion classification, KDD Cup 1999 dataset, network security
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