@article{(Open Science Index):https://publications.waset.org/pdf/5652, title = {Attacks Classification in Adaptive Intrusion Detection using Decision Tree}, author = {Dewan Md. Farid and Nouria Harbi and Emna Bahri and Mohammad Zahidur Rahman and Chowdhury Mofizur Rahman}, country = {}, institution = {}, abstract = {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.}, journal = {International Journal of Computer and Information Engineering}, volume = {4}, number = {3}, year = {2010}, pages = {368 - 372}, ee = {https://publications.waset.org/pdf/5652}, url = {https://publications.waset.org/vol/39}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 39, 2010}, }