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  7. The emergence of machine learning as a society-changing technology in the past decade has triggered concerns about people's inability to understand the reasoning of increasingly complex models. The field of IML (interpretable machine learning) grew out of these concerns, with the goal of empowering various stakeholders to tackle use cases, such as building trust in models, performing model debugging, and generally informing real human decision-making.
    Free, publicly-accessible full text available December 31, 2022
  8. Networks are today a critical infrastructure. Their resilience against attacks is thus crucial. Protecting networks requires a comprehensive security life-cycle and the deployment of different protection techniques. To make defenses more effective, recent solutions leverage AI techniques. In this paper, we discuss AI-based protection techniques, according to a security life-cycle consisting of several phases: (i) Prepare; (ii) Monitor and Diagnose; and (iii) React, Recovery and Fix. For each phase, we discuss relevant AI techniques, initial approaches, and research directions.
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