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Bayesian networks are very powerful tools for knowledge representation and reasoning under uncertainty. This paper shows the applicability of naive Bayesian classifiers to two major problems in intrusion detection: the detection of elementary attacks and the detection of coordinated ones. We propose two models starting with stating the problems and defining the variables necessary for model building...
In this paper, we address the problem of possibilistic network-based classification with uncertain inputs. Possibilistic networks are powerful tools for representing and reasoning with uncertain and incomplete information in the framework of possibility theory. We first consider the direct use of Jeffrey's rule in the framework of possibility theory in order to perform classification with uncertain...
Decision trees are well known and efficient classifiers widely used as behavioral approaches. However, most works pointed out their inefficiency in detecting novel attacks. In this paper, we address the inadequacy of decision trees for behavioral anomaly detection. We first explain why decision trees fail in detecting most of novel attacks. In particular, we provide experimental results showing that...
This paper deals with anomaly score aggregation and thresholding in multi-model anomaly-based approaches which require multiple detection models and profiles in order to characterize the different aspects of normal activities. Most works focus on profile/model definition while critical issues related to anomaly measuring, aggregating and thresholding have not received similar attention. In this paper,...
Decision trees and naive Bayes have been recently used as classifiers for intrusion detection problems. They present good complementarities in detecting different kinds of attacks. However, both of them generate a high number of false negatives. This paper proposes a hybrid classifier that exploits complementaries between decision trees and naive Bayes. In order to reduce false negative rate, we propose...
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