Intrusion Detection in SDN-Based Networks: Deep Recurrent Neural Network Approach

Software Defined Networking (SDN) is emerging as a key technology for future Internet. SDN provides a global network along with the capability to dynamically control network flow. One key advantage of SDN, as compared to the traditional network, is that by virtue of centralized control it allows better provisioning of network security. Nevertheless, the flexibility provided by SDN architecture manifests several new network security issues that must be addressed to strengthen SDN network security. So, in this paper, we propose a Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) enabled intrusion detection system for SDN. The proposed approach was tested using the NSL-KDD and CICIDS2017 dataset, and we achieved an accuracy of 89% and 99% respectively with low dimensional feature sets that can be extracted at the SDN controller. We also evaluated network performance of our proposed approach in terms of throughput and latency. Our test results show that the proposed GRU-RNN model does not deteriorate the network performance. Through extensive experimental evaluation, we conclude that our proposed approach exhibits a strong potential for intrusion detection in the SDN environments

Monitoring and maintenance of telecommunication systems : Challenges and research perspectives

In this paper, we present challenges associated with monitoring and maintaining a large telecom system at Ericsson that was developed with high degree of component reuse. The system constitutes of multiple services, composed of both legacy and modern systems that are constantly changing and need to be adapted to changing business needs. The paper is based on firsthand experience from architecting, developing and maintaining such a system, pointing out current challenges and potential avenues for future research that might contribute to addressing them. © Springer Nature Switzerland AG 2019