Character Level Based Detection of DGA Domain Names
Recently several different deep learning architectures have been proposed that take a string of characters as the raw input signal and automatically derive features for text classification. Little studies are available that compare the effectiveness of these approaches for character based text classification with each other. In this paper we perform such an empirical comparison for the important cybersecurity problem of DGA detection: classifying domain names as either benign vs. produced by malware (i.e., by a Domain Generation Algorithm). Training and evaluating on a dataset with 2M domain names shows that there is surprisingly little difference between various convolutional neural network (CNN) and recurrent neural network (RNN) based architectures in terms of accuracy, prompting a preference for the simpler architectures, since they are faster to train and less prone to overfitting.
 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.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Character Level Based Detection of DGA Domain Names
Recently several different deep learning architectures have been proposed that take a string of characters as the raw input signal and automatically derive features for text classification. Little studies are available that compare the effectiveness of these approaches for character based text classification with each other. In this paper we perform such an empirical comparison for the important cybersecurity problem of DGA detection: classifying domain names as either benign vs. produced by malware (i.e., by a Domain Generation Algorithm). Training and evaluating on a dataset with 2M domain names shows that there is surprisingly little difference between various convolutional neural network (CNN) and recurrent neural network (RNN) based architectures in terms of accuracy, prompting a preference for the simpler architectures, since they are faster to train and less prone to overfitting.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 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.
assistant
 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.