A new data mining based hybrid network Intrusion Detection model

Intrusion Detection System (IDS) plays an effective role to achieve higher security in detecting malicious activities, for a couple of years. To cope up with the requirements of continuous, heavy, incoming network traffic analysis, the classification model should be very fast. Naive Bayes is one of the classification models that predicts very fast due to the less complexity functioning of it. Fast prediction is also the reason for a lot work done in recent years using Bayesian approach. This paper proposes, a new hybrid model that ensembles Naive Bayes (statistical) and Decision Table Majority (rule based) approaches. The experimental results show better performance in detection rate as well false positive rate with reasonable prediction time.