Paper:
Malicious PDF files represent one of the biggest threats to computer security. To detect them, significant research has been done using handwritten signatures or machine learning based on manual feature extraction. Those approaches are both time-consuming, requires significant prior knowledge and the list of features has to be updated with each newly discovered vulnerability. In this work, we propose a novel algorithm that uses a Convolutional Neural Network (CNN) on the byte level of the file, without any handcrafted features. We show, using a data set of 130000 files, that our approach maintains a high detection rate (96%) of PDF malware and even detects new malicious files, still undetected by most antiviruses. Using automatically generated features from our CNN network, and applying a clustering algorithm, we also obtain high similarity between the antiviruses' labels and the resulting clusters.

Review:
The paper proposes to use CNN to detect malicious PDFs. The paper uses two simple CNN models and train them on a collected dataset, compare with other antivirus software, and conclude that CNN performs better. But I have serious concerns about the experiments.
1. The major concern is with the dataset. The dataset used by this work are collected by authors themselves, and I see a serious problem in this process. The malicious and benign ones are not collected from the same distribution: "The malicious samples are taken from VirusTotal (3). They were uploaded on the website between the 5/5/2018 and the 11/14/2018"; "The benign files were obtained using collaboration with a private company". Thus, the model may not be actually learning what's malicious and what's benign, but only learning whether the pdf comes from that private company or VirusTotal. It's enough since the test set is also collected this way. It can be fairly easy to distinguish between two different datasets, and the same reasoning applies here. Also, the authors are not making the dataset available due to privacy reasons, which further make the dataset's validity a question. 
2. Also, antivirus softwares are applicable on any pdf files, but the model trained with the dataset collected may not be useful in other circumstances. The comparison between them are not fair. It should compare the same model/software but on multiple different datasets to demonstrate the model's general applicability.
3. The experiments done in this work is also not of a satisfactory level. For example, there are some missing blanks in Table 1. Why is that? The figures (e.g., Fig 1/2) can be improved a lot. It did not include baseline and provide little information. The fonts are too small.
4. No major novelty is introduced. The work is an application paper using very simple CNNs on the malicious PDF detection problem. This itself does not make the paper bad but combined with the unconvincing experiments it's a serious weakness.
In summary, the paper lacks solid experimental results to make its conclusion convincing and its model generalizable. I vote for rejection of the paper.