Naive Bayes vs decision trees in intrusion detection systems
By Nahla Ben Amor, Salem Benferhat, Zied Elouedi. ACM Symposium on Applied Computing, 2004
586 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/16a778c5d83cce2f4c4af46efafb927e7d0d8e60
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
- Ian M. Molloy
- Mohammad Ali Salahuddin
- Cristina Nita-Rotaru
- Sara Ayoubi
- V. Varadharajan
- U. Tupakula
- Raouf Boutaba
- Kien A. Hua
In Computer Networks and Communications
- Mohammad Ali Salahuddin
- Cristina Nita-Rotaru
- Sara Ayoubi
- V. Varadharajan
- U. Tupakula
- Raouf Boutaba
- Kien A. Hua
- Ninghui Li
Citations in the corpus
- Using probabilistic generative models for ranking risks of Android apps (2012, methodology): “Machine Learning in Security : Naive Bayes has been extensively used both in the context of spam detection [25, 18, 16, 28] and anomaly detection [26, 4] in network traffic flows.”
- A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “[20] compare NB and DT also using KDD’99 dataset, and promote NB’s linear training and classification times as a competitive alternative to DT.”
- A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection (2016, methodology): “[91] uses a simple form of a Bayesian network that can be considered a Naïve Bayes classifier.”
- A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, methodology): “[120] [121] [123] [124] [122] [126] [126] [125] [147] [127]”
- Decision tree classifier for network intrusion detection with GA-based feature selection (2005, methodology): “Example approaches include decision trees [1][14][15][17][21][27], Genetic Algorithm and Genetic Programming [5][6][16][19][24], naive Bayes [ 1 ][26], kNN [18] and neural networks [4][8].”
- A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “With the KDD dataset, R2L attacks were detected with Neural Net, SVM, and Naïve Bayes where the Neural Net obtained maximal accuracy of 26.68% [409]–[411].”