SHIELD SPD metrics Sample Clauses

SHIELD SPD metrics. In the scope of the pilot project a subset of SPD metrics will be carried out; while a complete set of metrics for security, dependability and privacy will be developed during the project follow up, in order to cover all the SHIELD concepts as described below. The selected metrics will validate the approach of a metric-compliant SPD framework. STATIC DYNAMIC Problem dimen- sions Classes Cinematic State (e.g. movement) Not Cinematic State (e.g. shape) Identity (e.g. person, vehicle) Behavior (e.g. fleeing crowd) Assessment (e.g. panic) Prediction (e.g. threat) Objects Estimate Precision Estimate Precision Classification (POD / FAR) Precision +Classification Situations Estimate Precision Classification (POD / FAR) Precision +Classification +Assessment Threats Classification (POD / FAR) Precision +Classification +Assessment +Prediction Table 2.1 – SPD Metrics examples SPD metrics will be a SHIELD key issue. First of all, the SHIELD project will identify static and dynamic SPD metrics driven by the requirements coming from the selected industrial scenarios, at each of the considered layers, as well as for the overall system. Then, the SHIELD project will identify the embedded system desired SPD level at the above-mentioned layers and for the overall system with respect to these metrics. The table below describes a possible approach for the selection of SHIELD metrics. Key parameters of each considered problem have been identified as dimension, class and static and/or dynamic nature. Moreover, an example of several metrics is presented with respect to the proposed problem dimensions. For instance, considering SHIELD railway application scenario such metrics will be applied to evaluate the required SPD levels in case of an act of terrorism as a malicious attack against the network of cameras which guarantees the surveillance of railway stations or the detection of a potentially dangerous unattended luggage (e.g. a bomb). Thus, in the proposed example, SHIELD framework situational-aware and context-aware capabilities will be analyzed with respect to precision, classification and assessment required metrics in order to provide a quantitative and qualitative evaluation of obtained SPD performances. Finally, it is relevant to point out that presented metrics can be applied to a large range of application scenarios aiming at validating and defining performance of SHIELD framework in terms of required SPD levels independently from a specific domain
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