Anomaly Network Intrusion Detection Based on Improved Self Adaptive Bayesian Algorithm

Recently, research on intrusion detection in computer systems has received much attention to the computational intelligence society. Many intelligence learning algorithms applied to the huge volume of complex and dynamic dataset for the construction of efficient intrusion detection systems (IDSs). Despite of many advances that have been achieved in existing IDSs, there are still some difficulties, such as correct classification of large intrusion detection dataset, unbalanced detection accuracy in the high speed network traffic, and reduce false positives. This paper presents a new approach to the alert classification to reduce false positives in intrusion detection using improved self adaptive Bayesian algorithm (ISABA). The proposed approach applied to the security domain of anomaly based network intrusion detection, which correctly classifies different types of attacks of KDD99 benchmark dataset with high classification rates in short response time and reduce false positives using limited computational resources.