ISEP - DM - Engenharia de Sistemas Computacionais Críticos
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Browsing ISEP - DM - Engenharia de Sistemas Computacionais Críticos by Subject "Accuracy"
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- Deployment of ML Mechanisms for Cybersecurity in Resource-Constrained Embedded SystemsPublication . Vicente, Pedro Miguel Casal; Santos, Pedro Miguel Salgueiro dosThe increase of low security devices in the Internet is being exploited by hackers to compro mise data or use to use them as external agents to perform further attacks. As so, it is of crucial importance that networks posses a system that correctly assess the nature of incom ing and outgoing packets to protect the local network and the overall Internet connected systems. To achieve this, Machine Learning is being broadly used due to his early success. Nevertheless, these mechanisms are better inserted at the entry point of local networks, an embedded system which has limited resources to train machine learning models and/or to perform inference tasks. Since Cybersecurity is a real-time problem, the embedded systems should perform these activities in a very restricted time interval. The time required to clas sify the packets depends on the overall system load, machine learning models complexity and desired accuracy. This thesis aims to assess the current support for ML in embedded systems, either through the interoperability of models or through their development in low level languages, and the relationship between the time required by different embedded sys tems, the different tools and models. This thesis explored one transpilation tool, m2cgen, two interoperability formats, PMML and Open Neural Network Exchange (ONNX) and one real time environment, ONNXRuntime, to deploy an already trained model in a device with limited resources. Results demonstrate that ONNXRuntime was the only machine learn ing tool with a perfect match regarding samples prediction’s classification from the original models. An analysis on the time required to execute this task revealed that ONNXRun time is faster than Scikit-Learn with the Isolation Forest (ISO), One Class Support Vector Machine (OCSVM) and Stochastic Gradient Descent One Class Support Vector Machine (SGDOCSVM) models and slower with the Local Outlier Factor (LOF) model.