Biblio

Attackers create new threats and constantly change their behavior to mislead security systems. In this paper, we propose an adaptive threat detection architecture that trains its detection models in real time. The major contributions of the proposed architecture are: i) gather data about zero-day attacks and attacker behavior using honeypots in the network; ii) process data in real time and achieve high processing throughput through detection schemes implemented with stream processing technology; iii) use of two real datasets to evaluate our detection schemes, the first from a major network operator in Brazil and the other created in our lab; iv) design and development of adaptive detection schemes including both online trained supervised classification schemes that update their parameters in real time and learn zero-day threats from the honeypots, and online trained unsupervised anomaly detection schemes that model legitimate user behavior and adapt to changes. The performance evaluation results show that proposed architecture maintains an excellent trade-off between threat detection and false positive rates and achieves high classification accuracy of more than 90%, even with legitimate behavior changes and zero-day threats.

Control plane distribution on Software Defined Networking enhances security, performance and scalability of the network. In this paper, we propose an efficient architecture for distribution of controllers. The main contributions of the proposed architecture are: i) A controller distributed areas to ensure security, performance and scalability of the network; ii) A single database maintained by a designated controller to provide consistency to the control plane; iii) An optimized heuristic for locating controllers to reduce latency in the control plane; iv) A resilient mechanism of choosing the designated controller to ensure the proper functioning of the network, even when there are failures. A prototype of the proposal was implemented and the placement heuristic was analyzed in real topologies. The results show that connectivity is maintained even in failure scenarios. Finally, we show that the placement optimization reduces the average latency of controllers. Our proposed heuristic achieves a fair distribution of controllers and outperforms the network resilience of other heuristics up to two times better.

Named-Data Networking (NDN) is the most prominent proposal for a clean-slate proposal of Future Internet. Nevertheless, NDN routing schemes present scalability concerns due to the required number of stored routes and of control messages. In this work, we present a controller-based routing protocol using a formal method to unambiguously specify, and validate to prove its correctness. Our proposal codes signaling information on content names, avoiding control message overhead, and reduces router memory requirements, storing only the routes for simultaneously consumed prefixes. Additionally, the protocol installs a new route on all routers in a path with a single route request to the controller, avoiding replication of routing information and automating router provisioning. As a result, we provide a protocol proposal description using the Specification and Description Language and we validate the protocol, proving that CRoS behavior is free of dead or live locks. Furthermore, the protocol validation guarantees that the scheme ensures a valid working path from consumer to producer, even if it does not assure the shortest path.