Temporal Aspect Aware Graph Neural Network in Cybersecurity

Žít v dynamickém světě znamená řešit časově závislé úlohy. Avšak moderní nástroje pro strojové učení na grafech jsou především navržené pro statické sítě. Proto se v této závěrečné práci detailně zabývám problematikou strojového učení respektujícího časový aspekt pro grafové úlohy. Výsledkem tohoto teoretického výzkumu je návrh dynamické grafové neuronové sítě se spojitým časem. Zaměřuji se na problém Cisco Cognitive Intelligence maliciousness classification --- úlohu odhalení internetových domén s bezpečnostním rizikem na základě interakcí mezi uživateli a doménami. Ukazuji, že tento problém lze efektivně vyřešit použitím různých přístupů strojového učení, včetně navrženého. Navíc demonstruji, že obecné zákonitostí bezpečnostního rizika domén nevykazují dynamické vlastnosti v uvažovaných datech z reálného světa.Living in a dynamic world means dealing with time-dependent tasks. However, the modern toolbox for machine learning on graphs is mainly designed for static networks. Therefore, in this thesis, I deepen into the problematics of temporal-aware machine learning approaches for graph problems. The outcome of this study is a proposal for the new continuous-time dynamic graph neural network. I focus on the Cisco Cognitive Intelligence maliciousness classification problem --- the task of malicious Internet domain exposure based on user-domain interactions. I demonstrate that this problem can be efficiently solved employing different approaches, including the proposed one. Moreover, I show that general maliciousness patterns do not exhibit dynamic properties in the considered real-world data

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

Aggregated search: a new information retrieval paradigm

International audienceTraditional search engines return ranked lists of search results. It is up to the user to scroll this list, scan within different documents and assemble information that fulfill his/her information need. Aggregated search represents a new class of approaches where the information is not only retrieved but also assembled. This is the current evolution in Web search, where diverse content (images, videos, ...) and relational content (similar entities, features) are included in search results. In this survey, we propose a simple analysis framework for aggregated search and an overview of existing work. We start with related work in related domains such as federated search, natural language generation and question answering. Then we focus on more recent trends namely cross vertical aggregated search and relational aggregated search which are already present in current Web search