Biblio

Error correction in quantum cryptography based on artificial neural networks is a new and promising solution. In this paper the security verification of this method is discussed and results of many simulations with different parameters are presented. The test scenarios assumed partially synchronized neural networks, typical for error rates in quantum cryptography. The results were also compared with scenarios based on the neural networks with random chosen weights to show the difficulty of passive attacks.

The most significant threats to networks usually originate from external entities. As such, the Northbound interface of SDN networks which ensures communication with external applications requires particularly close attention. In this paper we propose the Risk Assessment and Management approach to SEcure SDN (RAMSES). This novel solution is able to estimate the risk associated with traffic demand requests received via the Northbound-API in SDN networks. RAMSES quantifies the impact on network cost incurred by expected traffic demands and specifies the likelihood of adverse requests estimated using the reputation system. Accurate risk estimation allows SDN network administrators to make the right decisions and mitigate potential threat scenarios. This can be observed using extensive numerical verification based on an network optimization tool and several scenarios related to the reputation of the sender of the request. The verification of RAMSES confirmed the usefulness of its risk assessment approach to protecting SDN networks against threats associated with the Northbound-API.