EISM-CPS: An Enhanced Intelligent Security Methodology for Cyber-Physical Systems through Hyper-Parameter Optimization
نویسندگان
چکیده
The increased usage of cyber-physical systems (CPS) has gained the focus cybercriminals, particularly with involvement internet, provoking an attack surface. these generates heavy data flows, which must be analyzed to ensure security. In particular, machine learning (ML) and deep (DL) algorithms have shown feasibility promising results fulfill security requirement through adoption intelligence. However, performance models strongly depends on model structure, hyper-parameters, dataset, application. So, developers only possess control over defining structure its hyper-parameters for diversified applications. Generally, not all perform well in default hyper-parameter settings. Their specification is a challenging complex task requires significant expertise. This problem can mitigated by utilizing optimization (HPO) techniques, intend automatically find efficient specific applications or datasets. paper proposes enhanced intelligent mechanism CPS HPO. Specifically, exhaustive HPO techniques been considered evaluation computational requirements analyze their capabilities build effective cope infringements CPS. Moreover, we various normalization, feature selection. To HPO, evaluated effectiveness DL-based artificial neural network (ANN) standard dataset under manual settings such as random search, directed grid Bayesian optimization. We utilized min-max algorithm normalization SelectKBest performed better than They achieved accuracy, precision, recall, F1 score more 98%. highlight importance enhancement reduction requirements, human efforts,
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ژورنال
عنوان ژورنال: Mathematics
سال: 2022
ISSN: ['2227-7390']
DOI: https://doi.org/10.3390/math11010189