Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Adversarial attacks on SDN-based deep IDS <s> In this paper we design and evaluate a Deep-Reinforcement Learning agent that optimizes routing. Our agent adapts automatically to current traffic conditions and proposes tailored configurations that attempt to minimize the network delay. Experiments show very promising performance. Moreover, this approach provides important operational advantages with respect to traditional optimization algorithms. <s> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Adversarial attacks on SDN-based deep IDS <s> In recent years, many techniques have been developed to improve the performance and efficiency of data center networks. While these techniques provide high accuracy, they are often designed using heuristics that leverage domain-specific properties of the workload or hardware. ::: In this vision paper, we argue that many data center networking techniques, e.g., routing, topology augmentation, energy savings, with diverse goals actually share design and architectural similarity. We present a design for developing general intermediate representations of network topologies using deep learning that is amenable to solving classes of data center problems. We develop a framework, DeepConfig, that simplifies the processing of configuring and training deep learning agents that use the intermediate representation to learns different tasks. To illustrate the strength of our approach, we configured, implemented, and evaluated a DeepConfig-Agent that tackles the data center topology augmentation problem. Our initial results are promising --- DeepConfig performs comparably to the optimal. <s> BIB002
JSMA attack showed a significant impact on the deep ML models ranges from 14 to 42%, JSMA-RE reduced the accuracy of MLP to 35%. FGSM caused a significant reduction in the accuracy of LSTM (more than 50%). Stampa et al. BIB001 Deep deterministic policy gradients Routing Outperformed their primary benchmark. Streiffer et al. BIB002 A3C Automating data center network topologies management Finds near optimal solutions across a range of topologies.