An Intrusion Detection Model Based on Feature Reduction and Convolutional Neural Networks
By Yihan Xiao, Cheng Xing, Taining Zhang, Zhongkai Zhao. IEEE Access, 2019
302 citations recorded by the source corpus.
With the popularity and development of network technology and the Internet, intrusion detection systems (IDSs), which can identify attacks, have been developed. Traditional intrusion detection algorithms typically employ mining association rules to identify intrusion behaviors. However, they fail to fully extract the characteristic information of user behaviors and encounter various problems, such as high false alarm rate (FAR), poor generalization capability, and poor timeliness. In this paper, we propose a network intrusion detection model based on a convolutional neural network–IDS (CNN–IDS). Redundant and irrelevant features in the network traffic data are first removed using different dimensionality reduction methods. Features of the dimensionality reduction data are automatically extracted using the CNN, and more effective information for identifying intrusion is extracted by supervised learning. To reduce the computational cost, we convert the original traffic vector format into an image format and use a standard KDD-CUP99 dataset to evaluate the performance of the proposed CNN model. The experimental results indicate that the AC, FAR, and timeliness of the CNN–IDS model are higher than those of traditional algorithms. Therefore, the model we propose has not only research significance but also practical value.
Subfields: Artificial Intelligence, Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/8aca082eb0389274b76802c2074c963d7c973759
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In Artificial Intelligence
- Farhan Ahmad
- Johari Abdullah
- Ke He
- Dan Dongseong Kim
- Wai Shiang Cheah
- Muhammad Rizwan Asghar
- Adnan Shahid Khan
- Zeeshan Ahmad
In Computer Networks and Communications
Citations in the corpus
- Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey (2023, methodology): “Xiao et al. [50] used dimensionality reduction techniques and mapped the reduced features to a 2D matrix to make the input feature suitable for CNN.”
- Network intrusion detection system: A systematic study of machine learning and deep learning approaches (2020, methodology): “Xiao et al 123 proposed an efficient IDS based on the CNN.”