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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Related Experiment Videos

Risk Assessment for Mobile Systems Through a Multilayered Hierarchical Bayesian Network.

Shancang Li, Theo Tryfonas, Gordon Russell

    IEEE Transactions on Cybernetics
    |April 15, 2016
    PubMed
    Summary

    This study introduces a three-layer framework using a Bayesian risk graph to assess mobile application risks in Android systems. This approach quantifies app vulnerabilities and their propagation, enhancing overall mobile security.

    Related Experiment Videos

    Area of Science:

    • Computer Science
    • Cybersecurity
    • Software Engineering

    Background:

    • Mobile systems face significant security threats from combined application vulnerabilities.
    • Assessing individual mobile application (app) risks is crucial for understanding overall system security.
    • Existing methods may not fully capture the propagation of risks across different layers of mobile systems.

    Purpose of the Study:

    • To develop a novel three-layer framework for assessing risks introduced by apps in Android mobile systems.
    • To propose a Bayesian risk graphical model for evaluating risk propagation within a layered architecture.
    • To quantitatively analyze security risks faced by both apps and the entire mobile system.

    Main Methods:

    • Integration of static analysis, dynamic analysis, and behavior analysis within a hierarchical framework.
    • Development of a Bayesian risk graphical model to represent and evaluate risk propagation.
    • Quantitative analysis of risks across multiple layers of the mobile system architecture.

    Main Results:

    • The proposed framework effectively models risk propagation through a layered architecture using a Bayesian risk graph.
    • The Bayesian risk graph quantitatively analyzes risks associated with individual apps and the overall mobile system.
    • The hierarchical model provides a clear understanding of how vulnerabilities can propagate.

    Conclusions:

    • The hierarchical Bayesian risk graph model offers a novel approach to investigating mobile security risks.
    • This strategy enables users and administrators to effectively evaluate potential app-related risks.
    • The framework strengthens both individual app security and the security of the entire mobile system.