An Intelligent DDoS Attack Detection System Using Packet Analysis and Support Vector Machine

Nowadays, many companies and/or governments require a secure system and/or an accurate intrusion detection system (IDS) to defend their network services and the user's private information. In network security, developing an accurate detection system for distributed denial of service (DDoS) attacks is one of challenging tasks. DDoS attacks jam the network service of the target using multiple bots hijacked by crackers and send numerous packets to the target server. Servers of many companies and/or governments have been victims of the attacks. In such an attack, detecting the crackers is extremely difficult, because they only send a command by multiple bots from another network and then leave the bots quickly after command execute. The proposed strategy is to develop an intelligent detection system for DDoS attacks by detecting patterns of DDoS attack using network packet analysis and utilizing machine learning techniques to study the patterns of DDoS attacks. In this study, we analyzed large numbers of network packets provided by the Center for Applied Internet Data Analysis and implemented the detection system using a support vector machine with the radial basis function (Gaussian) kernel. The detection system is accurate in detecting DDoS attacks.