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Current intrusion detection systems are mostly based on typical data mining techniques. The growing prevalence of new network attacks represents a well-known problem which can impact the availability, confidentiality, and integrity of critical information for both individuals and enterprises. 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 aredoi:10.47277/ijcncs/2(7)1 fatcat:ha6agyvg5jcwtdneemdasyrjsq