text: This paper proposes to automatically recognize domain names as malicious or benign by deep networks (convnets and RNNs) trained to directly classify the character sequence as such. Pros The paper addresses an important application of deep networks, comparing the performance of a variety of different types of model architectures. The tested networks seem to perform reasonably well on the task. Cons There is little novelty in the proposed method/models  -- the paper is primarily focused on comparing existing models on a new task. The descriptions of the different architectures compared are overly verbose  -- they are all simple standard convnet / RNN architectures.   The code specifying the models is also excessive for the main text  -- it should be moved to an appendix or even left for a code release. The comparisons between various architectures are not very enlightening  as they aren’t done in a controlled way  -- there are a large number of differences between any pair of models  so it’s hard to tell where the performance differences come from.  It’s also difficult to compare the learning curves among the different models (Fig 1)  as they are in separate plots with differently scaled axes. The proposed problem is an explicitly adversarial setting  and adversarial examples are a well-known issue with deep networks and other models,  but this issue is not addressed or analyzed in the paper.  (In fact, the intro claims this is an advantage of not using hand-engineered features for malicious domain detection, seemingly ignoring the literature on adversarial examples for deep nets.)  For example, in this case an attacker could start with a legitimate domain name and use black box adversarial attacks (or white box attacks, given access to the model weights) to derive a similar domain name that the models proposed here would classify as benign. While this paper addresses an important problem,  in its current form the novelty and analysis are limited  and the paper has some presentation issues.