Deep learning approach for Network Intrusion Detection in Software Defined Networking
Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments.
Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach
The developments of connected vehicles are heavily influenced by information and communications technologies, which have fueled a plethora of innovations in various areas, including networking, caching, and computing. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on vehicular networks. In this paper, we propose an integrated framework that can enable dynamic orchestration of networking, caching, and computing resources to improve the performance of next generation vehicular networks. We formulate the resource allocation strategy in this framework as a joint optimization problem, where the gains of not only networking but also caching and computing are taken into consideration in the proposed framework. The complexity of the system is very high when we jointly consider these three technologies. Therefore, we propose a novel deep reinforcement learning approach in this paper. Simulation results with different system parameters are presented to show the effectiveness of the proposed scheme.
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