Detection of distributed service attacks in SDN using ML



A software-defined network (SDN) is a network architecture that used to build, design the hardware components virtually. We can dynamically change the settings of network connections. In the traditional network, it’s not possible to change dynamically, because it’s a fixed connection. SDN is a good approach but still is vulnerable to DDoS attacks. The DDoS attack is menacing to the internet. To prevent the DDoS attack, the machine learning algorithm can be used. The DDoS attack is the multiple collaborated systems that are used to target the particular server at the same time. In SDN control layer is in the center that link with the application and infrastructure layer, where the devices in the infrastructure layer controlled by the software. In this paper, we propose a machine learning technique namely Decision Tree and Support Vector Machine (SVM) to detect malicious traffic. Our test outcome shows that the Decision Tree and Support Vector Machine (SVM) algorithm provides better accuracy and detection rate.


Software-Defined Networking is an emerging paradigm that overcomes the limitations of conventional network architecture by separating the control from data plane devices. SDN consists of three planes such as data plane, control plane and application plane. Data plane carries the network traffic based on the decision made by the controller. The Control plane decides the flow of traffic by computing the routing tables. Application plane manages the other applications like load balancer, firewalls, Quality of Service (QoS) applications, etc. SDN architecture improves the network performance by decoupling the network control and forward function. The control programs running in a logically centralized controller will control multiple routers across the network.


The SDN provides the sole ability to the applications to get to know the entire network information. During high traffic, the integration of different applications helps for load balancing and intrusion detection. If an anomaly is detected, the controller is instructed by the application to reprogram the data plane to alleviate it. Both control and data plane runs on routers that are distributed across the network, where the devices have open interfaces that can be controlled by the software

 Existing system:

In this section, we analyze the various research works done in the field of SDN to detect the DDoS attacks. By analyzing the various research works, we have identified that there are various techniques to avert the DDoS attack i.e. Random forest, Naive bayes, KNN, Neural Network, SVM, SOM. In the proposed work, Support Vector Machine (SVM) and decision tree algorithms are used to detect the DDoS attacks by analyzing the essential features of traffic.


.The experiment result shows the  not accuracy

.More complexity



               In this section, we discuss about our proposed work for identifying DDoS attacks using ML in SDN. We have used SVM and Decision tree algorithm to detect the attacks due to its accurate classification and less complexity



Detection of distributed denial of service attacks in SDN using machine learning techniques
Detection of distributed denial of service attacks in SDN using machine learning techniques


             . The experiment result shows the accuracy

.To detect the attacks due to its accurate classification

. Less complexity

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