Machine-assisted Cyber Threat Analysis using Conceptual Knowledge Discovery

Over the last years, computer networks have evolved into highly dynamic and interconnected environments, involving multiple heterogeneous devices and providing a myriad of services on top of them. This complex landscape has made it extremely difficult for security administrators to keep accurate and be effective in protecting their systems against cyber threats. In this paper, we describe our vision and scientific posture on how artificial intelligence techniques and a smart use of security knowledge may assist system administrators in better defending their networks. To that end, we put forward a research roadmap involving three complimentary axes, namely, (I) the use of FCA-based mechanisms for managing configuration vulnerabilities, (II) the exploitation of knowledge representation techniques for automated security reasoning, and (III) the design of a cyber threat intelligence mechanism as a CKDD process. Then, we describe a machine-assisted process for cyber threat analysis which provides a holistic perspective of how these three research axes are integrated together

Elements About Exploratory, Knowledge-Based, Hybrid, and Explainable Knowledge Discovery

International audienceKnowledge Discovery in Databases (KDD) and especially pattern mining can be interpreted along several dimensions, namely data, knowledge, problem-solving and interactivity. These dimensions are not disconnected and have a direct impact on the quality, applicability, and efficiency of KDD. Accordingly, we discuss some objectives of KDD based on these dimensions, namely exploration, knowledge orientation, hybridization, and explanation. The data space and the pattern space can be explored in several ways, depending on specific evaluation functions and heuristics, possibly related to domain knowledge. Furthermore, numerical data are complex and supervised numerical machine learning methods are usually the best candidates for efficiently mining such data. However, the work and output of numerical methods are most of the time hard to understand, while symbolic methods are usually more intelligible. This calls for hybridization, combining numerical and symbolic mining methods to improve the applicability and interpretability of KDD. Moreover, suitable explanations about the operating models and possible subsequent decisions should complete KDD, and this is far from being the case at the moment. For illustrating these dimensions and objectives, we analyze a concrete case about the mining of biological data, where we characterize these dimensions and their connections. We also discuss dimensions and objectives in the framework of Formal Concept Analysis and we draw some perspectives for future research

Pattern Structures and Concept Lattices for Data Mining and Knowledge Processing

International audienceThis article aims at presenting recent advances in Formal Concept Analysis (2010-2015), especially when the question is dealing with complex data (numbers, graphs, sequences, etc.) in domains such as databases (functional dependencies), data-mining (local pattern discovery), information retrieval and information fusion. As these advances are mainly published in artificial intelligence and FCA dedicated venues, a dissemination towards data mining and machine learning is worthwhile

PUBLIC POLICIES, LAW, COMPLEXITIES AND NETWORKS

International audienceWhatever the sector-codification and management of legal norms, climate change regime, governance and multilateralism, social-ecological interactions, health, natural resource managementlaw and public policies form complex systems resulting from the diversity of agents, resources, norms and principles they imply, and from the multiplicity of processes and activities that contribute to the evolution of the state of affairs. The complexity is also demonstrated by the poor control that stakeholders and decision-makers have over the impacts of the instruments deployed and over the responses to their implementation. In such a context, as evidenced by the studies gathered in this volume, the methods deployed to interpret, understand or explain the law or the public policies in action multiply the types of approaches and the means solicited for their study. However, an emerging trend not only provides analytical tools, but also inspires several approaches to phenomena related to law and public policy. It consists in apprehending these phenomena in terms of various networks, supports for change, intricate exchanges, knowledge and innovation, management, but besides essential ingredients of the incessant, sometimes labile, interactions between the systemic components

On-demand Relational Concept Analysis

International audienceFormal Concept Analysis (FCA) and its associated conceptual structures are used to support exploratory search through conceptual navigation. Relational Concept Analysis (RCA) is an extension of Formal Concept Analysis to process relational datasets. RCA and its multiple interconnected structures represent good candidates to support exploratory search in relational datasets, as they are enabling navigation within a structure as well as between the connected structures. However, building the entire structures does not present an efficient solution to explore a small localised area of the dataset, to retrieve the closest alternatives to a given query. In these cases, generating only a concept and its neighbour concepts at each navigation step appears as a less costly alternative. In this paper, we propose an algorithm to compute a concept, and its neighbourhood, in connected concept lattices. The concepts are generated directly from the relational context family, and possess both formal and relational attributes. The algorithm takes into account two RCA scaling operators and it is implemented in the RCAExplore tool

Formal Concept Analysis: From Knowledge Discovery to Knowledge Processing

International audienceIn this chapter, we introduce Formal Concept Analysis (FCA) and some of its extensions. FCA is a formalism based on lattice theory aimed at data analysis and knowledge processing. FCA allows the design of so-called concept lattices from binary and complex data. These concept lattices provide a realistic basis for knowledge engineering and the design of knowledge-based systems. Indeed, FCA is closely related to knowledge discovery in databases, knowledge representation and reasoning. Accordingly, FCA supports a wide range of complex and intelligent tasks among which classification, information retrieval, recommendation, network analysis, software engineering and data management. Finally, FCA is used in many applications demonstrating its growing importance in data and knowledge sciences