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A dynamic risk-based access control architecture for cloud computing

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Abstract

Cloud computing is a distributed computing model that still faces problems. New ideas emerge to take advantage of its features and among the research challenges found in the cloud, we can highlight Identity and Access Management. The main problems of the application of access control in the cloud are the necessary flexibility and scalability to support a large number of users and resources in a dynamic and heterogeneous environment, with collaboration and information sharing needs. This paper proposes the use of risk-based dynamic access control for cloud computing. The proposal is presented as an access control model based on an extension of the XACML standard with three new components: the Risk Engine, the Risk Quantification Web Services and the Risk Policies. The risk policies present a method to describe risk metrics and their quantification, using local or remote functions. The risk policies allow users and cloud service providers to define how to handle risk-based access control for their resources, using different quantification and aggregation methods. The model reaches the access decision based on a combination of XACML decisions and risk analysis. A prototype of the model is implemented, showing it has enough expressivity to describe the models of related work. In the experimental results, the prototype takes between 2 and 6 milliseconds to reach access decisions using a risk policy. A discussion on the security aspects of the model is also presented.
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A dynamic risk-based access control
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Conference Location :
Krakow, Poland
Digital Object Identifier :
10.1109/NOMS.2014.6838319
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IEEE
Cloud computing is a distributed computing model that still faces problems. New ideas emerge to
take advantage of its features and among the research challenges found in the cloud, we can
highlight Identity and Access Management. The main problems of the application of access control in
the cloud are the necessary flexibility and scalability to support a large number of users and
resources in a dynamic and heterogeneous environment, with collaboration and information sharing
needs. This paper proposes the use of risk-based dynamic access control for cloud computing. The
proposal is presented as an access control model based on an extension of the XACML standard
with three new components: the Risk Engine, the Risk Quantification Web Services and the Risk
Policies. The risk policies present a method to describe risk metrics and their quantification, using
local or remote functions. The risk policies allow users and cloud service providers to define how to
handle risk-based access control for their resources, using different quantification and aggregation
methods. The model reaches the access decision based on a combination of XACML decisions and
risk analysis. A prototype of the model is implemented, showing it has enough expressivity to
describe the models of related work. In the experimental results, the prototype takes between 2 and
6 milliseconds to reach access decisions using a risk policy. A discussion on the security aspects of
the model is also presented.
Published in:
Network Operations and Management Symposium (NOMS), 2014 IEEE
Date of Conference:
5-9 May 2014
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Santos, Daniel Ricardo dos ; Networks and Management Laboratory, Federal University of Santa Catarina, Florianópolis - Brazil ; Westphall, Carla Merkle ;
Westphall, Carlos Becker
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... This model permits or denies access requests dynamically based on the estimated risk value [9]. This model performs risk analysis on each user access request to make the access decision [17]. The essential stage of implementing a risk-based access control model is the risk estimation process. ...
... The risk-based access control model is one of the dynamic models that use the security risk value associated with each access request as a criterion to determine the access decision. It performs a risk analysis to estimate the security risk value for each access request and then uses the estimated risk value to decide either granting or denying access [17,42]. ...
Article
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The risk-based access control model is one of the dynamic models that use the security risk as a criterion to decide the access decision for each access request. This model permits or denies access requests dynamically based on the estimated risk value. The essential stage of implementing this model is the risk estimation process. This process is based on estimating the possibility of information leakage and the value of that information. Several researchers utilized different methods for risk estimation but most of these methods were based on qualitative measures, which cannot suit the access control context that needs numeric and precise risk values to decide either granting or denying access. Therefore, this paper presents a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) model for risk estimation in the risk-based access control model for the Internet of Things (IoT). The proposed ANFIS model was implemented and evaluated against access control scenarios of smart homes. The results demonstrated that the proposed ANFIS model provides an efficient and accurate risk estimation technique that can adapt to the changing conditions of the IoT environment. To validate the applicability and effectiveness of the proposed ANFIS model in smart homes, ten IoT security experts were interviewed. The results of the interviews illustrated that all experts confirmed that the proposed ANFIS model provides accurate and realistic results with a 0.713 in Cronbach’s alpha coefficient which indicates that the results are consistent and reliable. Compared to existing work, the proposed ANFIS model provides an efficient processing time as it reduces the processing time from 57.385 to 10.875 Sec per 1000 access requests, which demonstrates that the proposed model provides effective and accurate risk evaluation in a timely manner.
... It can represent nonlinear functions of arbitrary complexity. Based on researcher [30], the fuzzy approach is enhanced with the risk assessment formula to compute the risk factor. Figure 9 shows the fuzzy risk model. ...
... A preliminary analysis is conducted on current risk models to address the Fog-IoT network challenges. A dynamic access control model is used to quantify the risk factor because it considers access policies and dynamic contextual features that can estimate in real-time [30]. This is apt in the Fog-IoT network since it is a distributed and flexible network. ...
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The Internet of Things (IoT) allows billions of physical objects to be connected to gather and exchange information to offer numerous applications. It has unsupported features such as low latency, location awareness, and geographic distribution that are important for a few IoT applications. Fog computing is integrated into IoT to aid these features to increase computing, storage, and networking resources to the network edge. Unfortunately, it is faced with numerous security and privacy risks, raising severe concerns among users. Therefore, this research proposes a contextual risk-based access control model for Fog-IoT technology that considers real-time data information requests for IoT devices and gives dynamic feedback. The proposed model uses Fog-IoT environment features to estimate the security risk associated with each access request using device context, resource sensitivity, action severity, and risk history as inputs for the fuzzy risk model to compute the risk factor. Then, the proposed model uses a security agent in a fog node to provide adaptive features in which the device’s behaviour is monitored to detect any abnormal actions from authorised devices. The proposed model is then evaluated against the existing model to benchmark the results. The fuzzy-based risk assessment model with enhanced MQTT authentication protocol and adaptive security agent showed an accurate risk score for seven random scenarios tested compared to the simple risk score calculations.
... The risk of an ACP reflects the aggregated risk of the permissions that are permitted by it. ACP modelling or optimization methods can use risk minimization as an objective to reduce the impact of excessive permission assignments (Jin et al., 2016;Dos Santos et al., 2014). High risk is also an indicator for high maintenance priority and can serve as context information for policy engineers and reviewers, based on the assumption that a high risk value suggests a more restrictive handling than a low one (Fuchs et al., 2014). ...
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Organizations undertake complex and costly projects to model high-quality Access Control Policies (ACPs). Once built, these policies must be maintained and managed in an ongoing process to keep their quality high. Insufficient maintenance leads to inaccurate authorization decisions and increases the policies’ administrative effort and susceptibility to errors. While the initial modeling of ACPs has received significant research interest, their optimization is not yet covered as broadly. This work provides a theoretical foundation for ACP quality and its optimization. Furthermore, it analyzes how existing research addresses optimization of ACPs with regard to six crucial optimization dimensions. It presents a structured literature survey tracing these optimization dimensions, the contributed research artifact and data requirements. Building on this literature catalogue, this work elaborates on inaccuracies for user permission assignments, data availability, minimal perturbation and recommendation-based optimization.
... To address the above problem, Chen et al. designed a dynamic risk-based access control model by adding risk engines, risk authorization services, and risk policies to XACML [31]. Dos Santos et al. designed a risk matrix for managing users' access to the cloud and proposed a corresponding risk-based access control model [32]. To adaptively compute trust and risk values in dynamic occasions, Shaikh et al. proposed two dynamic risk-based decision methods for access control systems, which could not only allow broader authorities under certain controlled conditions (e.g., if users showed a positive record of use toward the resources they acquired in the past, they can be allowed broader authorities) but also restrict legitimate access of bad authorized users [33]. ...
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Full-text available
Granting users precise access rights is one of the purposes of access control technologies. With the increasing requirements of fine-grained authorization, too strict or too loose access control models may cause many problems. In this paper, aiming at insufficient authorizations in text databases, we propose a risk-aware topic-based access control (RTBAC) model, which uses topics to represent the content relationships between users and data. The RTBAC model also uses risk technologies to grant users corresponding access rights based on their historical behaviours and their access requests. The RTBAC model is a fine-grained access control model, and the authorization of RTBAC can reach the paragraph level. Experimental results show that RTBAC is an efficient access control model and the access control granularity of the RTBAC model is more than 3 times that of the existing content-based access control models.
... The risk-based access control model is one of the dynamic models that use the security risk value associated with each access request as a criterion to determine the access decision. It performs a risk analysis to estimate the security risk value for each access request, and then it uses the estimated risk value to decide to either grant or deny access [4,6]. ...
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Providing a dynamic access control model that uses real-time features to make access decisions for IoT applications is one of the research gaps that many researchers are trying to tackle. This is because existing access control models are built using static and predefined policies that always give the same result in different situations and cannot adapt to changing and unpredicted situations. One of the dynamic models that utilize real-time and contextual features to make access decisions is the risk-based access control model. This model performs a risk analysis on each access request to permit or deny access dynamically based on the estimated risk value. However, the major issue associated with building this model is providing a dynamic, reliable, and accurate risk estimation technique, especially when there is no available dataset to describe risk likelihood and impact. Therefore, this paper proposes a Neuro-Fuzzy System (NFS) model to estimate the security risk value associated with each access request. The proposed NFS model was trained using three learning algorithms: Levenberg–Marquardt (LM), Conjugate Gradient with Fletcher–Reeves (CGF), and Scaled Conjugate Gradient (SCG). The results demonstrated that the LM algorithm is the optimal learning algorithm to implement the NFS model for risk estimation. The results also demonstrated that the proposed NFS model provides a short and efficient processing time, which can provide timeliness risk estimation technique for various IoT applications. The proposed NFS model was evaluated against access control scenarios of a children’s hospital, and the results demonstrated that the proposed model can be applied to provide dynamic and contextual-aware access decisions based on real-time features.
Chapter
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