Intrusion detection and prevention system using K-learning classification in cloud

In this era of technology when everything is available at just one click; Security is a big issue. Hackers and intruders are getting smarter. There are various methods to protect the network infrastructure as well as communication over the internet, for example firewalls, virtual private networks and encryption. Intrusion detection is comparatively innovative approach to such techniques. By using IDS, we can gather and use information from acknowledged attacks and figure out if somebody is trying to attack the network/host. In this paper, we have classified different types of IDS. Also we have proposed a hybrid model for intrusion detection and prevention system for cloud infrastructure, which has improved the feature of detecting the unidentified attack with anomaly-based detection along with a module which will try to diminish the false alarm rate in the network or system. Our proposed system finds random attacks in network and prevents these attacks automatically using the mechanism.