Biblio

In today's time Software Defined Network (SDN) gives the complete control to get the data flow in the network. SDN works as a central point to which data is administered centrally and traffic is also managed. SDN being open source product is more prone to security threats. The security policies are also to be enforced as it would otherwise let the controller be attacked the most. The attacks like DDOS and DOS attacks are more commonly found in SDN controller. DDOS is destructive attack that normally diverts the normal flow of traffic and starts the over flow of flooded packets halting the system. Machine Learning techniques helps to identify the hidden and unexpected pattern of the network and hence helps in analyzing the network flow. All the classified and unclassified techniques can help detect the malicious flow based on certain parameters like packet flow, time duration, accuracy and precision rate. Researchers have used Bayesian Network, Wavelets, Support Vector Machine and KNN to detect DDOS attacks. As per the review it's been analyzed that KNN produces better result as per the higher precision and giving a lower falser rate for detection. This paper produces better approach of hybrid Machine Learning techniques rather than existing KNN on the same data set giving more accuracy of detecting DDOS attacks on higher precision rate. The result of the traffic with both normal and abnormal behavior is shown and as per the result the proposed algorithm is designed which is suited for giving better approach than KNN and will be implemented later on for future.

Secure group communication systems have become increasingly important for many emerging network applications. An efficient and robust group key management approach is indispensable to a secure group communication system. Motivated by the theory of hyper-sphere, this paper presents a new group key management approach with a group controller (GC). In our new design, a hyper-sphere is constructed for a group and each member in the group corresponds to a point on the hyper-sphere, which is called the member's private point. The GC computes the central point of the hyper-sphere, intuitively, whose “distance” from each member's private point is identical. The central point is published such that each member can compute a common group key, using a function by taking each member's private point and the central point of the hyper-sphere as the input. This approach is provably secure under the pseudo-random function (PRF) assumption. Compared with other similar schemes, by both theoretical analysis and experiments, our scheme (1) has significantly reduced memory and computation load for each group member; (2) can efficiently deal with massive membership change with only two re-keying messages, i.e., the central point of the hyper-sphere and a random number; and (3) is efficient and very scalable for large-size groups.