Accurate explanations of how a trained neural network (NN) behaves are desirable for a wide range of labor-intensive activities, including troubleshooting, validation, and understanding performance issues or identifying biases. We address the problem of explaining feedforward piecewise NNs (such as CNNs with Relu) by breaking them down into their linear components. Automatic encoding of the NN structure into linear program constraints is used to extract continuous exact explanations, which help interpret model behavior over continuous regions, provide model descriptions that convey the importance of input features and answer model queries that analyze the feature contributions under user-defined constraints. Our examples show that we can extract explanations for NNs with a moderate number of layers without relying on approximations. We demonstrate that the high-level explanations can help understand the outputs of NNs and the comparative importance of features by observing the linear models. We also show how explaining continuous inputs prevents certain attacks that have been proposed against existing explainers.

As more applications of (deep) neural networks emerge in the computer networking domain, the correctness and predictability of a neural agent's behavior for corner case inputs are becoming crucial. Enabling the formal analysis of agents with nontrivial properties, we bridge between specifying intended high-level behavior and expressing low-level statements directly encoded into an efficient verification framework. Our results support that within minutes, one can establish the resilience of a neural network to adversarial attacks on its inputs, as well as formally prove properties that were previously relying on educated guesses. Finally, we also show how formal verification can help create an accurate visual representation of an agent behavior to perform visual inspection and improve its trustworthiness.

Software-Defined eXchanges (SDXes) promise to improve the interdomain routing ecosystem through SDN deployment. Yet, the naïve deployment of SDN on the Internet raises concerns about the correctness of the interdomain data-plane. By allowing operators to deflect traffic from default BGP routes, SDN policies can create permanent forwarding loops that are not visible to the control-plane.