Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm
By Mohammed A. Ambusaidi, Xiangjian He, Priyadarsi Nanda, Zhiyuan Tan. IEEE transactions on computers, 2016
561 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/d7257e47cdd257fa73af986342fd966d047b8e50
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
- Mohamed Mosbah
- V. Varadharajan
- U. Tupakula
- Akka Zemmari
- Ke He
- Dan Dongseong Kim
- Emmanuel S. PIlli
- Parvez Faruki
In Computer Networks and Communications
- Mohamed Mosbah
- V. Varadharajan
- U. Tupakula
- Akka Zemmari
- Ke He
- Dan Dongseong Kim
- Emmanuel S. PIlli
- Parvez Faruki
Citations in the corpus
- A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, background): “[137] [138] [156] [140] [28] [141] [142] [143] [144] [159] [160]”
- Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, methodology): “It is necessary tool for analyzing high dimensional [135], noisy data.”
- Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey (2023, background): “, information gain), and correlation coefficient [73], [74], [75].”
- CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection (2020, methodology): “[23] propose a supervised filtered-based features selection algorithm called Flexible Mutual Information Feature Selection (FMIFS).”
- An Adaptive Ensemble Machine Learning Model for Intrusion Detection (2019, background): “[17] proposed a mutual information based algorithm that analytically selects the optimal feature for classification.”