Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99

By Hilmi Günes Kayacik, N. Zincir-Heywood, Malcolm I. Heywood. Conference on Privacy, Security and Trust, 2005

318 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/60e28c7da56eb61dd8ddb710a6f079ef02668014

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. V. Varadharajan
2. Monowar Hussain Bhuyan
3. U. Tupakula
4. Md. Abu Naser Bikas
5. Emmanuel S. PIlli
6. Jugal Kumar Kalita
7. Preeti Mishra
8. Mohammad Sazzadul Hoque

Citations in the corpus

• A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, methodology): “mised) [164] are most relevant features, calculated based on Rough Set theory and Information Gain measure respectively and hence should be included in the Feature Set.”
• An Implementation of Intrusion Detection System Using Genetic Algorithm (2012, methodology): “Finally, there is a sniffer that records all network traffic using the TCP dump format [32].”
• Network Anomaly Detection: Methods, Systems and Tools (2014, methodology): “There are various classes of intrusions or attacks [39], [40] in computer systems.”