Biblio

This article describes an approach to identification and certification in decentralized environment. The protocol proposes a way of integration for blockchain technology and web-of-trust concept to create decentralized public key infrastructure with flexible management for user identificators. Besides changing the current public key infrastructure, this system can be used in the Internet of Things (IoT). Each individual IoT sensor must correctly communicate with other components of the system it's in. To provide safe interaction, components should exchange encrypted messages with ability to check their integrity and authenticity, which is presented by this scheme.

The operation and management of network is facing increasing complexities brought by the evolution of network protocols and the demands of rapid service delivery. In this paper, we propose a programmable network management architecture, which manages network based on NETCONF protocol and provides REST APIs to upper layer so that further programming can be done based on the APIs to implement flexible management. Functions of devices can be modeled based on YANG language, and the models can be translated into REST APIs. We apply it to the management of ADN (Address Driven Network), an innovative network architecture proposed by Tsinghua University to inhibit IP spoofing, improve network security and provide high service quality. We model the functions of ADN based on YANG language, and implement the network management functions based on the REST APIs. We deploy and evaluate it in a laboratory environment. Test result shows that the programmable network management architecture is flexible to implement management for new network services.

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.