A Framework for Network Intrusion Detection in Cloud

Abstract

Due to on-demand, ubiquitous and shared resources facility, the cloud computing attract more user towards its services to use it. Cloud services are provided through the Internet, so there is possibility of attacks over the Internet. User to root, Denial of service(DoS) and Port scanning are the possible attacks over the Internet. These types of attacks are example of network intrusion. There is a need of machine learning techniques based model with high rate of detection and minimal rate of false alarm. For this, An intrusion detection model using an AdaBoost algorithm is proposed. For weak classifiers, we are using decision stumps in the algorithm. Strong classifier are built by merging the weak classifiers.