A novel intrusion detection system based on hierarchical clustering and support vector machines
By Shi-Jinn Horng, Ming-Yang Su, Yuan-Hsin Chen, T. Kao, Rong-Jian Chen, Jui-Lin Lai, C. D. Perkasa. Expert systems with applications, 2011
524 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/15d101b69e311f7ef90a85facaa9d2b63e41cacb
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Zhiyuan Tan
- Priyadarsi Nanda
- V. Varadharajan
- Xiangjian He
- U. Tupakula
- Mohammed A. Ambusaidi
- Emmanuel S. PIlli
- Preeti Mishra
In Computer Networks and Communications
- Zhiyuan Tan
- Priyadarsi Nanda
- V. Varadharajan
- Xiangjian He
- U. Tupakula
- Mohammed A. Ambusaidi
- Emmanuel S. PIlli
- Preeti Mishra
Citations in the corpus
- A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, methodology): “For example, Hierarchical Clustering with SVM technique [134] is considering 19 features and using KDD’99 dataset for detecting all attacks (DoS, Probe, U2R and R2L) and is achieving a detection rate of 99.”
- A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems (2015, background): “Connection records in KDD Cup 99 contain 41 features (Horng et al., 2011).”
- Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm (2016, methodology): “[17] proposed an SVM-based IDS, which combines a hierarchical clustering and the SVM.”