Analysis and Detection of DDoS Attacks on Cloud Computing Environment using Machine Learning Techniques

The primary benefit of the cloud is that it elastically scales to meet variable demand and it provides the environment which scales up and scales down instantly according to the demand, so it needs great protection from DDoS attacks to tackle downtime effects of DDoS Attacks. Distribute Denial of Service attacks fall on the category of critical attacks that compromise the availability of the network. These attacks have become sophisticated and continue to grow at a rapid pace so to detect and counter these attacks have become a challenging task. This work was carried out on the owncloud environment using Tor Hammer as an attacking tool and a new dataset was generated with Intrusion Detection System. This work incorporates various machine learning algorithms: Support Vector Machine, Naive Bayes, and Random Forest for classification and overall accuracy was 99.7%, 97.6% and 98.0% of Support Vector Machine, Random Forest and Naive Bayes respectively.