Hassan Takabi

Security and Privacy Challenges in Cloud Computing Environments

Cloud computing is an evolving paradigm with tremendous momentum, but its unique aspects exacerbate security and privacy challenges. This article explores the roadblocks and solutions to providing a trustworthy cloud computing environment.

SecureCloud: Towards a Comprehensive Security Framework for Cloud Computing Environments

Cloud computing has recently gained tremendous momentum but still is in its infancy. It has the potential for significant cost reduction and the increased operating efficiencies in computing. Although security issues are delaying its fast adoption, cloud computing is an unstoppable force and we need to provide security mechanisms to ensure its secure adoption. In this paper, we propose a comprehensive security framework for cloud computing environments. We also discuss challenges, existing solutions, approaches, and future work needed to provide a trustworthy cloud computing environment.

Towards active detection of identity clone attacks on online social networks

Online social networks (OSNs) are becoming increasingly popular and Identity Clone Attacks (ICAs) that aim at creating fake identities for malicious purposes on OSNs are becoming a significantly growing concern. Such attacks severely affect the trust relationships a victim has built with other users if no active protection is applied. In this paper, we first analyze and characterize the behaviors of ICAs. Then we propose a detection framework that is focused on discovering suspicious identities and then validating them. Towards detecting suspicious identities, we propose two approaches based on attribute similarity and similarity of friend networks. The first approach addresses a simpler scenario where mutual friends in friend networks are considered; and the second one captures the scenario where similar friend identities are involved. We also present experimental results to demonstrate flexibility and effectiveness of the proposed approaches. Finally, we discuss some feasible solutions to validate suspicious identities.

StateMiner: an efficient similarity-based approach for optimal mining of role hierarchy

Recently, there is a growing trend of organizations migrating to RBAC because of the economic benefits that RBAC provides, and the ease of administration. In order to deploy an RBAC system, one requires to first identify a complete set of roles. This process, known as role engineering, has been identified as one of the costliest tasks in migrating to RBAC. Several approaches have been proposed that mostly use data mining techniques to discover roles. However, most of them do not consider the existing roles and try to define everything from scratch, which is not acceptable for organizations that already have an RBAC system in place. In this paper, we formally define the problem of mining role hierarchy with minimal perturbation and present StateMiner, a heuristic solution to find an RBAC state as similar as possible to both the existing state and the optimal state. We present experiments to demonstrate the effectiveness of our approach.

Policy Management as a Service: An Approach to Manage Policy Heterogeneity in Cloud Computing Environment

Security issues are delaying fast adoption of cloud computing and security mechanisms to ensure its secure adoption has become a crucial immediate need. On the other hand, cloud computing can help enable security controls to be delivered in new ways by service providers. To this end, we need frameworks for efficient delivery of cloud-based security services and for provisioning desirable solutions to customers based on their requirements. In this paper, we focus on policy management systems in cloud environments. Currently, users must use diverse access control solutions available for each cloud service provider to secure data. Access control policies may be composed in incompatible ways because of diverse policy languages that are maintained separately at every cloud provider. Heterogeneity and distribution of these policies pose problems in managing access policy rules for a cloud environment. In this paper, we introduce Policy Management as a Service (PMaaS), a cloud based policy management framework that is designed to give users a unified control point for managing access policies to control access to his resources no matter where they are stored. We present the framework and describe its components and protocols needed for various components to communicate.

Trust-Based User-Role Assignment in Role-Based Access Control

Role based access control (RBAC) model is naturally suitable to organizations where users are assigned organizational roles with well-defined privileges. Nowadays, many organizations and enterprises such as banks, insurance industry and utility companies, provide online services to their very large number of users. This shows that assigning users to roles is a intolerable task and maintaining user-role assignment up-to-date is costly and error-prone. Also, with the increasing number of users, RBAC may have problems in prohibiting cheat and changing roles of users. To overcome these problems, user-role assignment decision can be made based on how much we trust him/her. In this paper, we propose a model to assign users to roles based on trustworthiness which is fuzzy in nature. The proposed model uses fuzzy relation equations to compute trust values.

Exploring reactive access control

As users store and share more digital content at home, access control becomes increasingly important. One promising approach for helping non-expert users create accurate access policies is reactive policy creation, in which users can update their policy dynamically in response to access requests that would not otherwise succeed. An earlier study suggested reactive policy creation might be a good fit for file access control at home. To test this, we conducted an experience-sampling study in which participants used a simulated reactive access-control system for a week. Our results bolster the case for reactive policy creation as one mode by which home users specify access-control policy. We found both quantitative and qualitative evidence of dynamic, situational policies that are hard to implement using traditional models but that reactive policy creation can facilitate. While we found some clear disadvantages to the reactive model, they do not seem insurmountable.

Semantic-based policy management for cloud computing environments

Cloud computing environments do not allow use of a single access control mechanism, single policy language or single policy management tool for various cloud services. Currently, users must use diverse access control solutions available for each cloud service provider to secure their data. Access control policies may be composed in incompatible ways because of diverse policy languages that are maintained separately at every cloud service provider. Heterogeneity and distribution of these policies pose problems in their administration. The semantic web technologies can provide the solution to interoperability of heterogeneous cloud service providers. In this paper, we introduce a semantic-based policy management framework that is designed to give users a unified control point for managing policies that control access to their data no matter where the data is stored. We present the framework and describe its components. Furthermore, we present a proof of concept implementation and results of performance evaluation.

Separation of Duty in Role-Based Access Control Model through Fuzzy Relations

As a security principle, separation of duty (SoD) is widely considered in computer security. In the role-based access control(RBAC) model, separation of duty constraints enforce conflict of interest policies. There are two main types of separation of duty policies in RBAC, Static SoD (SSoD) and Dynamic SoD (DSoD). In RBAC, Statically Mutually Exclusive Role (SMER) constraints are used to enforce Static Separation of Duty policies. Dynamic Separation of duty policies, like SSoD policies, are intended to limit the permissions that are available to a user. However, DSoD policies differ from SSoD policies by the context in which these limitations are imposed. A DSoD policy limits the availability of the permissions over a users permission space by placing constraints on the roles that can be activated within or across a users sessions. Like SMER, in RBAC Dynamically Mutually Exclusive Role (DMER) constraints are used to enforce DSoD policies. We investigated using of a fuzzy approach to address the issue in order to provide a more practical solution. In this paper, we propose a model to express the separation of duty policies in RBAC using the fuzzy set theory. The concept of trustworthiness, which is fuzzy in nature, is used to express this model. In comparison with non-fuzzy methods, our method is more pragmatic and more consistent with the real world. The expressiveness of our method is higher than the non- fuzzy ones. We show expression of some constraints in our method which cannot be expressed by non-fuzzy methods. Applicability of the method is shown through an example of the real world.

A comprehensive approach for network attack forecasting

We modify the attack graph to handle the uncertainty of attack probabilities.We analyze the IDS alerts and intrusion responses to update the attack probabilities.We define a forecasting attack graph to predict future attacks.The forecasting attack graph provides a high-level insight into the network security. Forecasting future attacks is a big challenge for network administrators because future is generally unknown. Nevertheless, some information about the future can help us make better decisions in present time. Attack graph is the most well-known tool for risk assessment and attack prediction. However, it only provides static information about probability of vulnerability exploitation, which is not reliable for predicting the future. Moreover, attack graph does not consider the uncertainty of probabilities. Therefore, the primary goal of this paper is to present an attack forecasting approach that can predict future network attacks with more precision and dynamically adapts to changes in the environment. Our proposed approach handles the uncertainty of attack probabilities and uses additional information, such as intrusion alerts, active responses, and dependency graph in the forecasting process. Experiments show that size and complexity of the proposed forecasting attack graph makes it suitable for predicting future attacks even in large-scale networks.