| Title | Intrusion Detection and Prevention in Software Defined Networking |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Abhilash, Goyal, Divyansh, Gupta |
| Conference Name | 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) |
| Keywords | Classification algorithms, composability, Computer architecture, computer network security, data plane, Intrusion detection, learning (artificial intelligence), machine learning, machine learning algorithms, Metrics, network controller, network intrusion detection, Network security, OpenFlow, pubcrawl, Resiliency, software defined networking, Support vector machines, Training |
| Abstract | Software defined networking is a concept proposed to replace traditional networks by separating control plane and data plane. It makes the network more programmable and manageable. As there is a single point of control of the network, it is more vulnerable to intrusion. The idea is to train the network controller by machine learning algorithms to let it make the intelligent decisions automatically. In this paper, we have discussed our approach to make software defined networking more secure from various malicious attacks by making it capable of detecting and preventing such attacks. |
| DOI | 10.1109/ANTS.2018.8710141 |
| Citation Key | abhilash_intrusion_2018 |
Intrusion Detection and Prevention in Software Defined Networking