Adaptive Security Based on MAPE-K: A Survey

Lara, Evangelina; Aguilar, Luis T.; Sánchez, Martínez; García, Jesús A.

doi:10.1007/978-3-030-17985-4_7

Cited by 4 publications

(5 citation statements)

References 27 publications

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“…An analysis with only Fuzzy approach researched by Alali et al [12] to identify the components of cyber risk, it then supported by MAPE-K approach on separate occasion [13]. Therefore, in this research, the Monitor, Analyze, Plan, Execute and Knowledge Base (MAPE-K) equipped with self-properties, which is the combination of Fuzzy and Q-Learning interacting with the assigned components iteratively within the loops [11].…”

Section: Evaluation VII Validationmentioning

confidence: 93%

An Active Cyber Insurance Policy Against Cybersecurity Risks Using Fuzzy Q-Learning

Ramli

2023

ARASET

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Insurance is an extremely diversified from one clear-cut policy to another depending on a rising demand. Internet based businesses are a booming industry and falls under categories of small, medium and enterprise. It is no exception that cybersecurity risks are exponentially presents as a continues fears in this business environment. To ensure this insecurity is handled effectively, cyber insurance policies introduced by insurance companies. However, the nature of cyber risks must be addressed in much more robust and complex algorithms. Autonomic computing with the combination of Fuzzy and Q-Learning is introduced to ensure active policies are ready to handle the uncertainty and together with the ability to learn and mitigate the unrest situation.

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Section: Evaluation VII Validationmentioning

confidence: 93%

An Active Cyber Insurance Policy Against Cybersecurity Risks Using Fuzzy Q-Learning

Ramli

2023

ARASET

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“…Monitor (step 1) is the first module that collects data about the environment and the managed resources, such as status, metrics, configuration settings, etc. Furthermore, the monitor compiles the data to identify symptoms/signs and sends these symptoms/signs to the Analyze module for further processing (Lara et al 2019). The analyze module (step 2) includes the mechanism for analyzing and reasoning the data sent by the Monitor.…”

Section: Self-adaptive Model Identificationmentioning

confidence: 99%

“…Moreover, the analysis module determines whether there is a need for adaptation. Analyze may use prediction frameworks or models (Lara et al 2019) for data analysis. If there is a need for adaptation, it sends a request to the Plan module.…”

Section: Self-adaptive Model Identificationmentioning

confidence: 99%

“…The Plan module (step 3) generates a plan for modifying the managed resource and then passes the plan to the Execute module (step 4). The Execute module executes a set of actions provided by the Plan module to adjust the managed resource following the generated plan (Lara et al 2019). The knowledge module (step 5) saves and maintains all the information from other MAPE-K model modules (Fig.…”

Section: Self-adaptive Model Identificationmentioning

confidence: 99%

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A conceptual framework for context-driven self-adaptive intelligent user interface based on Android

Ali,

Khan,

Mashkoor

et al. 2024

Cogn Tech Work

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Adaptive User Interface (AUI) can change its layout, appearance, and/or elements based on the needs of its user requirements and current usage context. The AUIs are used in state-of-the-art software products, applications for mobile devices, and websites. Moreover, AUI is an emerging research field in a mobile context, as it can enhance usability, performance, and user satisfaction. This study aims to propose a conceptual framework for developing a real-time self-adaptive user interface based on the Android Operating System (OS). Furthermore, the focus is on developing the core algorithms for the modules of the proposed framework. To evaluate the performance of the proposed framework, three case studies have been designed based on the daily and weekly activities of the user. Moreover, an expert-based validation approach is employed to obtain the expert’s feedback regarding the proposed framework. The result indicates that the proposed framework helps improve user satisfaction and experience by making an intelligent mobile device interface. The results of the framework’s evaluation and validation show the proposed framework’s feasibility and effectiveness. We conclude that the current work is beneficial in filling the identified research gap. Moreover, this research shows the significance of an adaptive interface in an Android OS-based context. In addition, it not only helps in improving the user interest and satisfaction but also enhances the overall performance of the mobile device.

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“…Un enfoque que se ha propuesto en los últimos años para hacer frente a esta heterogeneidad, incertidumbre y dinamismo es el adaptativo, capaz de ajustar controles de seguridad aplicados al contexto y estado del activo, y al riesgo que estos factores implican en un momento dado [1], [2]. Se suele trabajar con modelos que modifiquen de forma autónoma el diseño, despliegue, configuración o modo de uso de los controles, tomando estas decisiones de adaptación con diferentes criterios y estrategias.…”

Section: Introducci óNunclassified

Verificación remota de controles de seguridad en contextos de seguridad adaptativa

Calvo¹,

Beltrán²

2021

Colección Jornadas Y Congresos

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Adaptive Security Based on MAPE-K: A Survey

Cited by 4 publications

References 27 publications

An Active Cyber Insurance Policy Against Cybersecurity Risks Using Fuzzy Q-Learning

An Active Cyber Insurance Policy Against Cybersecurity Risks Using Fuzzy Q-Learning

A conceptual framework for context-driven self-adaptive intelligent user interface based on Android

Verificación remota de controles de seguridad en contextos de seguridad adaptativa

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