In previous work we introduced an approach for finding security requirements based on misuse activities (actions). This method starts from the activity diagram of a use case (or a sequence of use cases). Each activity is analyzed to see how it could be subverted to produce a misuse of information. This analysis results in a set of threats. We then consider which policies can stop or mitigate these threats. We now extend that approach to consider in the analysis the type of misuse (confidentiality, integrity ...) that can happen in each activity, the role of the attacker, and the context for the threat. This extended analysis results in a finer and more systematic way to find threats and we can identify now more threats. We also improve the way to find the policies to control these threats and we consider how to map the corresponding policies to security patterns. The information in each pattern helps in the selection of an optimal (or good) set of policies. Our extended approach can be conveniently incorporated in a methodology to build secure systems.
The Brazilian Supreme Court receives tens of thousands of cases each semester. Court employees spend thousands of hours to execute the initial analysis and classification of those cases-which takes effort away from posterior, more complex stages of the case management workflow. In this paper, we explore multimodal classification of documents from Brazil's Supreme Court. We train and evaluate our methods on a novel multimodal dataset of 6,510 lawsuits (339,478 pages) with manual annotation assigning each page to one of six classes. Each lawsuit is an ordered sequence of pages, which are stored both as an image and as a corresponding text extracted through optical character recognition. We first train two unimodal classifiers: a ResNet pretrained on ImageNet is fine-tuned on the images, and a convolutional network with filters of multiple kernel sizes is trained from scratch on document texts. We use them as extractors of visual and textual features, which are then combined through our proposed Fusion This preprint, which was originally written on 8 April 2021, has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this article is published in the International Journal on Document Analysis and Recognition (IJDAR), and
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