Use of decision trees and attributional rules in incremental learning of an intrusion detection model
Use of decision trees and attributional rules in incremental learning of an intrusion detection model
In this paper, we propose a Learnable Model for Anomaly Detection (LMAD), as an ensemble real-time intrusion detection model using incremental supervised machine learning techniques. Such techniques are utilized to detect new attacks. The proposed model is based on making use of two different machine learning techniques, namely, decision trees and attributional rules classifiers.