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書目名稱Graphical Models for Security影響因子(影響力)




書目名稱Graphical Models for Security影響因子(影響力)學(xué)科排名




書目名稱Graphical Models for Security網(wǎng)絡(luò)公開度




書目名稱Graphical Models for Security網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Graphical Models for Security被引頻次




書目名稱Graphical Models for Security被引頻次學(xué)科排名




書目名稱Graphical Models for Security年度引用




書目名稱Graphical Models for Security年度引用學(xué)科排名




書目名稱Graphical Models for Security讀者反饋




書目名稱Graphical Models for Security讀者反饋學(xué)科排名

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https://doi.org/10.1007/978-1-4757-3569-7form their jobs. Instead, in this paper, we propose to treat the insider threat by a detective measure that holds an insider accountable in case of violations. However, to enable accountability, we need to create causal models that support reasoning about the causality of a violation. Current securi

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Overstate

https://doi.org/10.1007/978-3-322-81828-7ck scenarios or to identify critical attacks or attack paths. However, existing frameworks do not provide systematic analyses on the asset-level like the probability of successful or near-successful attacks on specific assets. But these insights are important to enable decision-makers to make more i

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https://doi.org/10.1007/978-3-8349-9685-5threats, the cyber security assessment of IT infrastructures can foster a higher degree of security and resilience against cyber-attacks. Therefore, the use of attack simulations based on system architecture models is proposed. To reduce the effort of creating new attack graphs for each system under

interpose

https://doi.org/10.1007/978-3-322-86982-1 technical aspects and ignore the impact strategic human decisions have on the behavior of systems. Furthermore, the high degree of complexity and lack of historical data for probability estimations in case of new and emerging systems seriously limit the practical utility of traditional risk analysi

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Multi-Disciplinary Digital Signal Processinglt, but enriching them endangers their simplicity and subsequent ease of take up. We present an approach for reasoning about tainted data flows in design-level DFDs by putting them in context with other complementary usability and requirements models. We illustrate our approach using a pilot study,