A reliable database of Indicators of Compromise (IoC’s) is a cornerstone of almost every malware detection system. Building the database and keeping it up-to-date is a lengthy and often manual process where each IoC should be manually reviewed and labeled by an analyst. In this paper, we focus on an automatic way of identifying IoC’s intended to save analysts’ time and scale to the volume of network data. We leverage relations of each IoC to other entities on the internet to build a heterogeneous graph. We formulate a classification task on this graph and apply graph neural networks (GNNs) in order to identify malicious domains. Our experiments show that the presented approach provides promising results on the task of identifying high-risk malware as well as legitimate domains classification.

Blending of search results from several vertical sources became standard among web search engines. Similar scenarios appear in computational advertising, news recommendation, and other interactive systems. As such environments give only partial feedback, the evaluation of new policies conventionally requires expensive online A/B tests. Counterfactual approach is a promising alternative, nevertheless, it requires specific conditions for a valid off-policy evaluation. We release a large-scale, real-world vertical-blending dataset gathered bySeznam.cz web search engine. The dataset contains logged partial feedback with the corresponding propensity and is thus suited for counterfactual evaluation. We provide basic checks for validity and evaluate several learning methods.

Minimal solutions play a crucial role in description of all solutions to a fuzzy relational equation. The reason is that all solutions form a convex set with respect to (fuzzy) set inclusion; therefore, having all extremal solutions, we can represent the entire solution set as a union of intervals bounded from above by the greatest solution and from below by the minimal solutions. However, when computing the intervals, we obtain many duplicate solutions. The obvious question is as follows: Is there another way of representing the solution set, for instance, without the need of having all the minimal solutions in advance? We provide the positive answer to this question.