Title
A Probabilistic Approach for Network Intrusion Detection
Abstract
This study aims to propose a probabilistic approach for detecting network intrusions using Bayesian networks (BNs). Three variations of BN, namely, naive Bayesian network (NBC), learned BN, and handcrafted BN, were evaluated and from which, an optimal BN was obtained. A standard dataset containing 494020 records, a category for normal network traffics, and four major attack categories (denial of service, probing, remote to local, user to root and normal), were used in this study. The dataset went through an 80-20 split to serve the training and testing phases. 80% of the dataset were treated with a feature selection algorithm to obtain a set of features, from which the three BNs were constructed. During the evaluation phase, the remaining 20% of the dataset were used to obtain the classification accuracies of the BNs. The results show that the hand-crafted BN, in general, has outperformed NBC and Learned BN.
Year
DOI
Venue
2008
10.1109/AMS.2008.92
Asia International Conference on Modelling and Simulation
Keywords
Field
DocType
feature selection algorithm,belief networks,normal network traffic,network intrusion detection,learned bayesian network,standard dataset,bayes methods,probabilistic approach,bayesian ai,handcrafted bayesian network,hand-crafted bn,bayesian networks,evaluation phase,optimal bn,learned bn,computer networks,naive bayesian networks,bayesian network,network intrusion,classification accuracy,telecommunication security,normal network traffics,classification accuracies,security of data,data security,bayesian methods,feature selection,random variables,intrusion detection,testing,denial of service,artificial intelligence
Data mining,Network intrusion detection,Naive Bayes classifier,Feature selection,Denial-of-service attack,Computer science,Telecommunication security,Bayesian network,Artificial intelligence,Probabilistic logic,Machine learning
Conference
ISBN
Citations 
PageRank 
978-0-7695-3136-6
4
0.47
References 
Authors
5
3
Name
Order
Citations
PageRank
Kok-Chin Khor1363.05
Choo-Yee Ting29013.19
Somnuk Phon-Amnuaisuk319425.89