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Quantifying Cybersecurity-QoS Trade-Offs in Smart Hospitals: A Comparative Study Using CSPNs and Markovian Agent Models
No full textIn smart hospitals, achieving a balance between cybersecurity and quality of service (QoS) is a critical yet underexplored challenge. Cyberattacks can disrupt medical services, while overly aggressive countermeasures may degrade performance or availability, thus violating Service Level Agreements (SLAs). To quantify this tradeoff, we model a representative smart healthcare system using two formal approaches: Colored Stochastic Petri Nets (CSPNs) and Markovian Agent Models (MAMs). The CSPN captures fine-grained, concurrent behaviors and stochastic delays at the token level, while the MAM abstracts global system dynamics via differential equations. Through extensive simulations, we evaluate mitigation latency, resource saturation, and system responsiveness under cyberattack scenarios. Confidence intervals, computed from repeated CSPN runs, provide statistically grounded insight into SLA compliance variability, highlighting that a significant portion of mitigations exceed the defined threshold. Despite the potential for rapid mitigation, stochastic delays and concurrency often result in critical SLA violations. This dual-model approach enables a complementary analysis: CSPNs reveal short-term congestion and resource contention, whereas MAMs uncover long-term systemic trends. The study offers a reproducible framework for evaluating cyber-resilience in safety-critical environments
Improving the quality of e-commerce web service: what is important for the request scheduling algorithm?
No full textImproving Clustering Of Web Bot And Human Sessions By Applying Principal Component Analysis
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Characterizing Web Sessions Of E-Customers Interested In Traditional And Innovative Products
No full textEfficiency Analysis Of Resource Request Patterns In Classification Of Web Robots And Humans
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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