Recently, information security has become a key issue

in information technology as the number of computer security

breaches are exposed to an increasing number of security threats. A

variety of intrusion detection systems (IDS) have been employed for

protecting computers and networks from malicious network-based or

host-based attacks by using traditional statistical methods to new data

mining approaches in last decades. However, today s commercially

available intrusion detection systems are signature-based that are not

capable of detecting unknown attacks. In this paper, we present a

new learning algorithm for anomaly based network intrusion

detection system using decision tree algorithm that distinguishes

attacks from normal behaviors and identifies different types of

intrusions. Experimental results on the KDD99 benchmark network

intrusion detection dataset demonstrate that the proposed learning

algorithm achieved 98 detection rate (DR) in comparison with

other existing methods.-Recently, information security has become a key issue

in information technology as the number of computer security

breaches are exposed to an increasing number of security threats. A

variety of intrusion detection systems (IDS) have been employed for

protecting computers and networks from malicious network-based or

host-based attacks by using traditional statistical methods to new data

mining approaches in last decades. However, today s commercially

available intrusion detection systems are signature-based that are not

capable of detecting unknown attacks. In this paper, we present a

new learning algorithm for anomaly based network intrusion

detection system using decision tree algorithm that distinguishes

attacks from normal behaviors and identifies different types of

intrusions. Experimental results on the KDD99 benchmark network

intrusion detection dataset demonstrate that the proposed learning

algorithm achieved 98　detection rate (DR) in comparison with

other existing methods.