Biblio

Filters: Author is Gangadhar, S.  [Clear All Filters]
2018-02-02
Modarresi, A., Gangadhar, S., Sterbenz, J. P. G..  2017.  A framework for improving network resilience using SDN and fog nodes. 2017 9th International Workshop on Resilient Networks Design and Modeling (RNDM). :1–7.

The IoT (Internet of Things) is one of the primary reasons for the massive growth in the number of connected devices to the Internet, thus leading to an increased volume of traffic in the core network. Fog and edge computing are becoming a solution to handle IoT traffic by moving timesensitive processing to the edge of the network, while using the conventional cloud for historical analysis and long-term storage. Providing processing, storage, and network communication at the edge network are the aim of fog computing to reduce delay, network traffic, and decentralise computing. In this paper, we define a framework that realises fog computing that can be extended to install any service of choice. Our framework utilises fog nodes as an extension of the traditional switch to include processing, networking, and storage. The fog nodes act as local decision-making elements that interface with software-defined networking (SDN), to be able to push updates throughout the network. To test our framework, we develop an IP spoofing security application and ensure its correctness through multiple experiments.

2017-12-28
Gangadhar, S., Sterbenz, J. P. G..  2017.  Machine learning aided traffic tolerance to improve resilience for software defined networks. 2017 9th International Workshop on Resilient Networks Design and Modeling (RNDM). :1–7.

Software Defined Networks (SDNs) have gained prominence recently due to their flexible management and superior configuration functionality of the underlying network. SDNs, with OpenFlow as their primary implementation, allow for the use of a centralised controller to drive the decision making for all the supported devices in the network and manage traffic through routing table changes for incoming flows. In conventional networks, machine learning has been shown to detect malicious intrusion, and classify attacks such as DoS, user to root, and probe attacks. In this work, we extend the use of machine learning to improve traffic tolerance for SDNs. To achieve this, we extend the functionality of the controller to include a resilience framework, ReSDN, that incorporates machine learning to be able to distinguish DoS attacks, focussing on a neptune attack for our experiments. Our model is trained using the MIT KDD 1999 dataset. The system is developed as a module on top of the POX controller platform and evaluated using the Mininet simulator.