SVM Based Scheme for Predicting Number of Zombies in a DDoS Attack

In recent time, Internet or network services has gain popularity due to rapid growth in information and telecommunication technologies. Internet or network services become the mean for finance management, education, and global information service center for news, advertisements and many others. Denial of service attack and most particularly the distributed denial of service attack (DDoS) is most common and harmful threat to the Internet or network services. In order to design and develop reliable and secure network services, rapid detection and quick response to these attacks are major concern. In practice, there is no scheme that completely detects or prevents the DDoS attack. Predicting number of zombies in a DDoS attack is helpful to suppress the effect of DDoS attack by filtering and rate limiting the most suspicious attack sources or improve DDoS response system. In this paper, we present machine learning approach based on support vector machine for regression to predict the number of zombies in a DDoS attack. MATLAB implementation of support vector machine for regression and datasets generated using NS-2 network simulators running on Linux platform are used for training and testing. SVM for regression with various kernel function and other parameters are compared for their prediction performance using mean square error (MSE). Results show SVM based scheme have promising prediction performance for small dataset.