A hybrid machine learning approach to network anomaly detection
By Taeshik Shon, Jongsub Moon. Information Sciences, 2007
467 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/548ba88cfef75633cf1fad6c16e31b67f95226d6
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
- Giorgio Giacinto
- Fabio Roli
- Battista Biggio
- Igino Corona
- Mohammad Ali Salahuddin
- Yu-Feng Hsu
- Sara Ayoubi
- Wei-Yang Lin
In Computer Networks and Communications
- Fabio Roli
- Giorgio Giacinto
- Igino Corona
- Battista Biggio
- Mohammad Ali Salahuddin
- Yu-Feng Hsu
- Sara Ayoubi
- Wei-Yang Lin
Citations in the corpus
- Machine Learning Techniques for Anomaly Detection: An Overview (2013, background): “[53] Shon and Moon.”
- Intrusion detection by machine learning: A review (2009, methodology): “In particular, Internet has been used as an important component of business models (Shon & Moon, 2007).”
- Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection (2018, methodology): “The domain of using machine learning (specifically anomaly detection) for implementing NIDSs was extensively researched in the past [13], [14], [15], [16], [17].”
- Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues (2013, background): “In general, classification models can be designed as a mixture of misuse- and anomaly-based approaches in order to exploit their respective pros [138].”
- A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “A multi-class approach in SVM can be formulated as a series of single class classifiers, where the data is assigned to the class that maximizes an output function.”
- A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection (2016, methodology): “The approach described by Shon and Moon [101] is a framework for detection of novel attacks in network traffic.”
- CANN: An intrusion detection system based on combining cluster centers and nearest neighbors (2015, methodology): “Shon and Moon [30] GA + SVM DARPA 1999 Anomaly detection DR, FP, FN SVM Yes Shon et al.”