Low-rate false alarm intrustion detection system with genetic algorithm approach

Intrusion Detection Systems (IDS) play an important role in identifying possible attacks to the networks. Most algorithms create a model; and the classification is done based on that model. During the classification, one of the main concerns is increasing the accuracy and the reliability. Traditional intrusion detection methods have some problems such as high false alarm rate, low detection compatibility against the new attacks, and insufficient capacity of the analysis. The next issue related to the intrusion detection systems is the feature reduction, extraction, and selection. In this paper, an intrusion detection model was created by genetic algorithms (GA) and the construction of the classifying models based on the training data observed from the genetic algorithm by KNN (K-Nearest Neighbors) algorithm has been taken into consideration. In this algorithm, it is supposed to reduce the false alarm rate in comparison with the previous methods.