Читать книгу Intelligent Network Management and Control - Badr Benmammar - Страница 16

Decision trees are powerful and widespread nonparametric learning tools used for classification and prediction problems. Their purpose is to create a model that predicts the values of the target variable, relying on a set of sequences of decision rules deduced from learning data. Rai et al. (2016) have developed an algorithm based on the C4.5 decision tree approach. The most relevant characteristics are selected by means of information gain and the fractional value is selected so that it renders the classifier unbiased with respect to the most frequent values. In the work of Sahu and Babu (2015), a database referred to as ”Kyoto 2006+” is used for the experiments. In Kyoto 2006+, each instance is labeled as “normal” (no attack), “attack” (known attack) and “unknown attack”. The Decision Tree algorithm (J48) is used to classify the packets. Experiments confirm that the generated rules operate with 97.2% accuracy. Moon et al. (2017) proposed an intrusion detection system based on decision trees using packet behavior analysis to detect the attacks. Peng et al. (2018) proposed a technique that involves a preprocessing for data digitization, followed by their normalization, in order to improve detection efficiency. Then a method based on decision trees is used.