A security evaluation framework for cloud security auditing

2017 ◽  
Vol 74 (11) ◽  
pp. 5774-5796 ◽  
Author(s):  
Syed Rizvi ◽  
Jungwoo Ryoo ◽  
John Kissell ◽  
William Aiken ◽  
Yuhong Liu
Keyword(s):  
Cloud Security ◽  
Evaluation Framework ◽  
Security Evaluation

A user oriented cloud security evaluation framework

2015 ◽  
Vol 3 (2) ◽  
pp. 168
Author(s):  
Syed Rizvi ◽  
Kelsey Karpinski ◽  
Brennen Kelly ◽  
Taryn Walker
Keyword(s):  
Cloud Security ◽  
Evaluation Framework ◽  
Security Evaluation

A Practical Conflicting Role-based Cloud Security Risk Evaluation Method

2019 ◽  
Vol 13 ◽  
Author(s):  
Jin Han ◽  
Jing Zhan ◽  
Xiaoqing Xia ◽  
Xue Fan
Keyword(s):  
Risk Evaluation ◽  
Evaluation Method ◽  
Service Providers ◽  
Risk Preferences ◽  
Cloud Service ◽  
Cloud Security ◽  
Third Party ◽  
Security Evaluation ◽  
Security Risk ◽  
Cloud Users

Background: Currently, Cloud Service Provider (CSP) or third party usually proposes principles and methods for cloud security risk evaluation, while cloud users have no choice but accept them. However, since cloud users and cloud service providers have conflicts of interests, cloud users may not trust the results of security evaluation performed by the CSP. Also, different cloud users may have different security risk preferences, which makes it difficult for third party to consider all users' needs during evaluation. In addition, current security evaluation indexes for cloud are too impractical to test (e.g., indexes like interoperability, transparency, portability are not easy to be evaluated). Methods: To solve the above problems, this paper proposes a practical cloud security risk evaluation method of decision-making based on conflicting roles by using the Analytic Hierarchy Process (AHP) with Aggregation of Individual priorities (AIP). Results: Not only can our method bring forward a new index system based on risk source for cloud security and corresponding practical testing methods, but also can obtain the evaluation result with the risk preferences of conflicting roles, namely CSP and cloud users, which can lay a foundation for improving mutual trusts between the CSP and cloud users. The experiments show that the method can effectively assess the security risk of cloud platforms and in the case where the number of clouds increased by 100% and 200%, the evaluation time using our methodology increased by only by 12% and 30%. Conclusion: Our method can achieve consistent decision based on conflicting roles, high scalability and practicability for cloud security risk evaluation.

scholarly journals A Security Evaluation Framework for U.K. E-Government Services Agile Software Development

2016 ◽  
Vol 8 (2) ◽  
pp. 51-69 ◽  
Author(s):  
Steve Harrison ◽  
Antonis Tzounis ◽  
Leandros Maglaras ◽  
Francois Siewe ◽  
Richard Smith ◽  
...  
Keyword(s):  
Software Development ◽  
Agile Software Development ◽  
Evaluation Framework ◽  
Security Evaluation ◽  
Government Services ◽  
Agile Software

scholarly journals A Cyber Security Evaluation Framework for In-Vehicle Electrical Control Units

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Haichun Zhang ◽  
Yuqian Pan ◽  
Zhaojun Lu ◽  
Jie Wang ◽  
Zhenglin Liu
Keyword(s):  
Cyber Security ◽  
Evaluation Framework ◽  
Security Evaluation ◽  
Electrical Control ◽  
Control Units

Valoración de la plataforma ASEF como base para detección de malware en aplicaciones Android

Ingenium ◽  
2014 ◽  
Vol 8 (21) ◽  
pp. 11
Author(s):  
Manuel Fernando Fuentes Amaya ◽  
Johan Alberto Gómez Girón
Keyword(s):  
Evaluation Framework ◽  
Security Evaluation ◽  
Android Security

El Android Security Evaluation Framework [ASEF] fue seleccionado por el proyecto Safe Candy como punto de partida para el desarrollo de un aplicativo para control de malware en teléfonos inteligentes con sistema operativo Android, debido a sus características, presentadas en Black-Hat 2012, que lo mostraban como una herramienta poderosa, innovadora y de gran potencial. Como primera actividad, el proyecto realizó una valoración del nivel de avance y la confiabilidad de ASEF, buscando determinar su validez como base de desarrollo e identificar el punto de partida para la generación de la aplicación esperada. Las pruebas de funcionamiento, exactitud y rapidez realizadas, mostraron debilidades y pendientes en el framework, resultado que generó la necesidad de redefinir la metodología prevista, con base en la cual el proyecto desarrolló un sistema para el análisis, la validación y el diagnóstico de aplicaciones Android, que integra –aunque solo de manera complementaria– a la plataforma ASEF. Al producto final se le realizó el mismo set de pruebas, obteniendo una tasa de éxito de 100%

Towards a Black-Box Security Evaluation Framework

2021 ◽  
pp. 79-92
Author(s):  
Mosabbah Mushir Ahmed ◽  
Youssef Souissi ◽  
Oualid Trabelsi ◽  
Sylvain Guilley ◽  
Antoine Bouvet ◽  
...  
Keyword(s):  
Black Box ◽  
Evaluation Framework ◽  
Security Evaluation

scholarly journals Machine Learning Based Automotive Forensic Analysis for Mobile Applications Using Data Mining

2015 ◽  
Vol 16 (2) ◽  
pp. 350
Author(s):  
MD. Hussain Khan ◽  
G. Pradeepini
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Mobile Apps ◽  
Forensic Analysis ◽  
Evaluation Framework ◽  
Security Evaluation ◽  
Analysis Framework ◽  
Security Issues ◽  
The People ◽  
Using Data

<p>Phone is a device which provides communication between the people through voice, text, video etc. Now a day’s people may leave without food but not without using phones. No of operating systems are working with various versions and various security issues are working. Security is very important task in Mobiles and mobile apps. To improve the security status of mobiles, existing methodology is using cloud computing and data mining. Out traditional method is named as MobSafe to identify the mobile apps antagonism or graciousness. In the proposed system, we adopt Android Security Evaluation Framework (ASEF) and Static Android Analysis Framework (SAAF).In this paper, our proposed system works on machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.</p>

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