The ever changing network traffic reveals new attack types, which represent a security threat that poses a serious risk for enterprise resources. Therefore, the security administrators are in a real need to employ efficient Intrusion Detection and Prevention Systems IDPS. Such systems might be capable to learn from the network behavior. In this paper, they present an incremental Learnable Model for Anomaly Detection and Prevention of Zero-day attacks LMAD/PZ. To facilitate the ability of learning from observations that can provide a reliable model for automatic prevention, a comparison has been carried out between supervised and unsupervised learning techniques.