Mutated Policies

Rubio-Medrano, Carlos E.; Lamp, Josephine; Doupé, Adam; Zhao, Ziming; Ahn, Gail-Joon

doi:10.1145/3140549.3140553

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…This model makes access control management and control more efficient by reducing the need for administrator intervention. Moreover, we reinforce the proposed model by suggesting a Moving Target Defense (MTD) technique [13] that provides an extra layer of security for deterring current and upcoming threats to defense policies. More precisely, the suggested MTD takes as an input an origin authorization to generate the origin attributes listed in the policy and to inspect the attribute bag.…”

Section: Introductionmentioning

confidence: 56%

“…In [13], Rubio et al presented a new MTD-inspired technique that permits companies to proactively collect the attributes of different entities involved in the access request process. After collection, the authors attempted to choose the attributes that would specifically identify the entities above and to modify the original accesses randomly over time by inserting additional policies built from the reidentified attributes.…”

Section: B Related Workmentioning

confidence: 99%

“…In this step, an environment constraint check is performed; if satisfied, access to data is allowed, and otherwise, the role revocation and session are ended. Then, while accessing the object, that condition is checked; if the environment condition becomes unchecked, this authorization is removed from the last available authorization, which the MTD system reinforces the ABAC method [13], where this system represents an additional layer of protection for a future attack on policies. The set of flows from the proposed system is shown in Fig.…”

Section: Access Decisionmentioning

confidence: 99%

“…This technique is inspired by the approach presented by Rubio et al in [13] Figure 3. A graphical representation of our approach reinforced by technique MTD.…”

Section: E Moving Target Defenses ( Policy Mutation)mentioning

confidence: 99%

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HyARBAC: A New Hybrid Access Control Model for Cloud Computing

Houhou,

Bitam,

Hamida

2024

IJCDS

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Cloud computing has been among the most critical digital processes and storage technologies in recent years. Nevertheless, the cloud is faced with data security issues due to its vulnerability to weak access control. Access control guarantees accessing cloud data and resources only to authorized users; unauthorized users are then detected and prevented from retrieving these resources. There have been many models of access control presented and used in the cloud domain, such as Attribute Based Access Control (ABAC) and Role Based Access Control (RBAC); however, these schemes suffered from many limitations, such as their difficulty using contextual information such as time, location of the user, type of device or the use of attributes residing in multiple and disparate locations, leading the performance of such approaches to be extremely low. To deal with these problems, we present a novel model of access control applied to Cloud computing called Hybrid attribute and role-based access control for cloud (HyARBAC). This proposal combines ABAC and RBAC to provide flexible and accurate access control that considers environmental information when controlling access and decreases administrator intervention to manage and control this task. In addition, this model is reinforced by integrating a moving target defense mechanism (MTD), considered an extra layer of security for deterring current and upcoming threats and trying to compromise the original attributes and their corresponding mechanisms of our authorization policies. After an experimental study applied to a healthcare database and comparison against ABAC and RBAC according to several performance features, the effectiveness of HyARBAC was proved to enhance access control.

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Section: Introductionmentioning

confidence: 56%

Section: B Related Workmentioning

confidence: 99%

Section: Access Decisionmentioning

confidence: 99%

“…This technique is inspired by the approach presented by Rubio et al in [13] Figure 3. A graphical representation of our approach reinforced by technique MTD.…”

Section: E Moving Target Defenses ( Policy Mutation)mentioning

confidence: 99%

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