Biblio

With current Traditional / Legacy networks, the reliance on manual intervention to solve a variety of issues be it primary operational functionalities like addressing Link-failure or other consequent complexities arising out of existing solutions for challenges like Link-flapping or facing attacks like DDoS attacks is substantial. This physical and manual approach towards network configurations to make significant changes result in very slow updates and increased probability of errors and are not sufficient to address and support the rapidly shifting workload of the networks due to the fact that networking decisions are left to the hands of physical networking devices. With the advent of Software Defined Networking (SDN) which abstracts the network functionality planes, separating it from physical hardware – and decoupling the data plane from the control plane, it is able to provide a degree of automation for the network resources and management of the services provided by the network. This paper explores some of the aspects of automation provided by SDN capabilities in a Mesh Network (provides Network Security with redundancy of communication links) which contribute towards making the network inherently intelligent and take decisions without manual intervention and thus take a step towards Intelligent Automated Networks.

Mesh topology networks provide Network security in the form of redundancy of communication links. But redundancy also contributes to complexity in configuration and subsequent troubleshooting. Mesh topology deployed in Critical networks like Backbone Networks (used in Cloud Computing) deploy the Mesh topology provides additional security in terms of redundancy to ensure availability of services. One amongst most prominent attacks is Distributed Denial of Service attacks which cause an immense amount of loss of data as well as monetary losses to service providers. This paper proposes a method by which using SDN capabilities and sFlow-RT application, Distributed Denial of Service (DDoS) attacks is detected and consequently mitigated by using REST API to implement Policy Based Flow Management (PBFM) through the SDN Controller which will help in ensuring uninterrupted services in scenarios of such attacks and also further simply and enhance the management of Mesh architecture-based networks.

Dozens of signature and anomaly based solutions have been proposed to detect malicious activities in computer networks. However, the number of successful attacks are increasing every day. In this paper, we developed a novel entropy based technique, called Edmund, to detect and mitigate Network attacks. While analyzing full payload network traffic was not recommended due to users' privacy, Edmund used netflow data to detect abnormal behavior. The experimental results showed that Edmund was able to highly accurate detect (around 95%) different application, transport, and network layers attacks. It could identify more than 100K malicious flows raised by 1168 different attackers in our campus. Identifying the attackers, is a great feature, which enables the network administrators to mitigate DDoS effects during the attack time.