Risk Adaptive Authorization Mechanism (RAdAM) for Cloud Computing

Fall, Doudou; Okuda, Takashi; Kadobayashi, Youki; Yamaguchi, Suguru

doi:10.2197/ipsjjip.24.371

Cited by 12 publications

(7 citation statements)

References 17 publications

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“…Lakshmi et al proposed a model for the identification of insider attackers by adjusting the session based on risk assessment. 20,21 Fall et al 22 also proposed a risk-adaptive authorization mechanism to satisfy the dynamicity in the cloud environment.…”

Section: Related Workmentioning

confidence: 99%

Dynamic and semantic-aware access-control model for privacy preservation in multiple data center environments

Chen

Xing

et al. 2020

International Journal of Distributed Sensor Networks

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With the rapid development of intelligent perception and other data acquisition technologies in the Internet of things, large-scale scientific workflows have been widely used in geographically distributed multiple data centers to realize high performance in business model construction and computational processing. However, insider threats pose very significant privacy and security risks to systems. Traditional access-control models can no longer satisfy the reasonable authorization of resources in these new cross-domain environments. Therefore, a dynamic and semantic-aware access-control model is proposed for privacy preservation in multiple data center environments, which implements a semantic dynamic authorization strategy based on an anomaly assessment of users’ behavior sequences. The experimental results demonstrate that this dynamic and semantic-aware access-control model is highly dynamic and flexible and can improve the security of the application system.

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Section: Related Workmentioning

confidence: 99%

Dynamic and semantic-aware access-control model for privacy preservation in multiple data center environments

Chen

Xing

et al. 2020

International Journal of Distributed Sensor Networks

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“…Furthermore, in this work we are not limiting our experiments to a single system or application component failure, rather we attempt to predict several failures across the entire infrastructure. For more comprehensive review on other literatures the reader is referred to [28][29][30][31][32][33][34][35][36], [47][48][49][50][51][52][53].…”

Section: Related Workmentioning

confidence: 99%

Failure prediction using machine learning in a virtualised HPC system and application

et al. 2019

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Failure is an increasingly important issue in high performance computing and cloud systems. As large-scale systems continue to grow in scale and complexity, mitigating the impact of failure and providing accurate predictions with sufficient lead time remains a challenging research problem. Traditional existing fault-tolerance strategies such as regular checkpointing and replication are not adequate because of the emerging complexities of high performance computing systems. This necessitates the importance of having an effective as well as proactive failure management approach in place aimed at minimizing the effect of failure within the system. With the advent of machine learning techniques, the ability to learn from past information to predict future pattern of behaviours makes it possible to predict potential system failure more accurately. Thus, in this paper, we explore the predictive abilities of machine learning by applying a number of algorithms to improve the accuracy of failure prediction. We have developed a failure prediction model using time series and machine learning, and performed comparison based tests on the prediction accuracy. The primary algorithms we considered are the Support Vector Machine (SVM), Random Forest(RF), k-Nearest Neighbors (KNN), Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA). Experimental results indicates that the average prediction accuracy of our model using SVM when predicting fail

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“…Thus, the fidelity of Risk Adaptive Authorization Mechanism (RAdAM) implementation in cloud has been proposed by [30] to determine access decision and introduce adaptable algorithm in cloud computing. However, the capability of RadAM in managing cloud federation has not been studied extensively.…”

Section: Risk-adaptable Access Control (Radac)mentioning

confidence: 99%

Towards Secure Risk-Adaptable Access Control in Cloud Computing

Abdullah

Bakar

2018

ijacsa

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The emergence of pervasive cloud computing has supported the transition of physical data and machine into virtualization environment. However, security threat and privacy have been identified as a challenge to support the widespread adoption of cloud among user. Moreover, user awareness on the importance of cloud computing has increase the needs to safeguard the cloud by implementing access control that works on dynamic environment. Therefore, the emergence of Risk-Adaptable Access Control (RAdAC) as a flexible medium in handling exceptional access request is a great countermeasure to deal with security and privacy challenges. However, the rising problem in safeguarding users' privacy in RAdAC model has not been discussed in depth by other researcher. This paper explores the architecture of cloud computing and defines the existing solutions influencing the adoption of cloud among user. At the same time, the obscurity factor in protecting privacy of user is found within RAdAC framework. Similarly, the two-tier authentication scheme in RAdAC has been proposed in responding to security and privacy challenges as shown through informal security analysis.

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Risk Adaptive Authorization Mechanism (RAdAM) for Cloud Computing

Cited by 12 publications

References 17 publications

Dynamic and semantic-aware access-control model for privacy preservation in multiple data center environments

Dynamic and semantic-aware access-control model for privacy preservation in multiple data center environments

Failure prediction using machine learning in a virtualised HPC system and application

Towards Secure Risk-Adaptable Access Control in Cloud Computing

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