Real detection intrusion using supervised and unsupervised learning

Advances in software and networking technologies have nowadays brought about innumerable benefits to both individuals and organizations. Along with technological explosions, there ironically exist numerous potential cyber-security breaches, thus advocating attackers to devise hazardous intrusion tactics against vulnerable information systems. Such security-related concerns have motivated many researchers to propose various solutions to face the continuous growth of cyber threats during the past decade. Among many existing IDS methodologies, data mining has brought a remarkable success in intrusion detection. However, data mining approaches for intrusion detection have still confronted numerous challenges ranging from data collecting and feature processing to the appropriate choice of learning methods and parametric thresholds. Hence, designing efficient IDS´s remains very tough. In this paper, we propose a new intrusion detection system by combining unsupervised and supervised learning method. Results shows the performance of this system.