A fundamental management responsibility is securing information systems. Almost all applications that deal with safety, privacy, or defense include some form of access control. There are a plethora of access control models in the information security realm such as role-based access control and attribute-based access control. However, the initial development of access control policies (ACPs) can be very challenging. Most organizations have high-level requirement specifications that include a set of ACPs, which describe allowable operations of the system. It is time consuming and error-prone to manually sift through these documents and extract ACPs. In this paper, we propose a new framework towards extracting ACPs from unrestricted natural language documents using semantic role labeling (SRL). We were able to correctly identify ACP elements with an average F1 score of 75 percent, which bested the previous work by 15 percent. Furthermore, as SRL tools are often trained on publicly available corpora such as Wall Street Journal, we investigated the idea of improving SRL performance using domain-related knowledge. We utilized domain adaptation and semi-supervised learning techniques and were able to improve the SRL performance by 2 percent using only a small amount of access control data.

Deep Neural Networks (DNNs) have overtaken classic machine learning algorithms due to their superior performance in big data analysis in a broad range of applications. On the other hand, in recent years Machine Learning as a Service (MLaaS) has become more widespread in which a client uses cloud services for analyzing its data. However, the client's data may be sensitive which raises privacy concerns. In this paper, we address the issue of privacy preserving classification in a Machine Learning as a Service (MLaaS) settings and focus on convolutional neural networks (CNN). To achieve this goal, we develop new techniques to run CNNs over encrypted data. First, we design methods to approximate commonly used activation functions in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree polynomials which is essential for a practical and efficient solution. Then, we train CNNs with approximation polynomials instead of original activation functions and implement CNNs classification over encrypted data. We evaluate the performance of our modified models at each step. The results of our experiments using several CNNs with a varying number of layers and structures are promising. When applied to the MNIST optical character recognition tasks, our approach achieved 99.25% accuracy which significantly outperforms state-of-the-art solutions and is close to the accuracy of the best non-private version. Furthermore, it can make up to 164000 predictions per hour. These results show that our approach provides accurate, efficient, and scalable privacy-preserving predictions in CNNs.

The National Institute of Standards and Technology (NIST) has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy (NLACP) to a machine-readable form. To study the automation process, we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations. Therefore, this paper focuses on the questions of: how can we automatically infer the hierarchical structure of an ABAC model given NLACPs; and, how can we extract and define the set of authorization attributes based on the resulting structure. To address these questions, we propose an approach built upon recent advancements in natural language processing and machine learning techniques. For such a solution, the lack of appropriate data often poses a bottleneck. Therefore, we decouple the primary contributions of this work into: (1) developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts, and (2) generating a set of realistic synthetic natural language access control policies (NLACPs) to evaluate the proposed framework. Our experimental results are promising as we achieved - in average - an F1-score of 0.96 when extracting attributes values of subjects, and 0.91 when extracting the values of objects’ attributes from natural language access control policies.

The National Institute of Standards and Technology (NIST) has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy (NLACP) to a machine-readable form. An essential step towards this automation is to automate the extraction of ABAC attributes from NLACPs, which is the focus of this paper. We, therefore, raise the question of: how can we automate the task of attributes extraction from natural language documents? Our proposed solution to this question is built upon the recent advancements in natural language processing and machine learning techniques. For such a solution, the lack of appropriate data often poses a bottleneck. Therefore, we decouple the primary contributions of this work into: (1) developing a practical framework to extract ABAC attributes from natural language artifacts, and (2) generating a set of realistic synthetic natural language access control policies (NLACPs) to evaluate the proposed framework. The experimental results are promising with regard to the potential automation of the task of interest. Using a convolutional neural network (CNN), we achieved - in average - an F1-score of 0.96 when extracting the attributes of subjects, and 0.91 when extracting the objects' attributes from natural language access control policies.

Abstract Machine learning algorithms based on deep Neural Networks (NN) have achieved remarkable results and are being extensively used in different domains. On the other hand, with increasing growth of cloud services, several Machine Learning as a Service (MLaaS) are offered where training and deploying machine learning models are performed on cloud providers’ infrastructure. However, machine learning algorithms require access to the raw data which is often privacy sensitive and can create potential security and privacy risks. To address this issue, we present CryptoDL, a framework that develops new techniques to provide solutions for applying deep neural network algorithms to encrypted data. In this paper, we provide the theoretical foundation for implementing deep neural network algorithms in encrypted domain and develop techniques to adopt neural networks within practical limitations of current homomorphic encryption schemes. We show that it is feasible and practical to train neural networks using encrypted data and to make encrypted predictions, and also return the predictions in an encrypted form. We demonstrate applicability of the proposed CryptoDL using a large number of datasets and evaluate its performance. The empirical results show that it provides accurate privacy-preserving training and classification.

Attribute-based access control (ABAC) is a logical access control methodology where authorization to perform a set of operations is based on attributes of the user, the objects being accessed, the environment, and a number of other attribute sources that may be relevant to the current request. Once fully implemented within an enterprise, ABAC promotes information sharing while maintaining control of the information. However, the cost of developing ABAC policies can be a significant obstacle for organizations to migrate from traditional access control models to ABAC. Most organizations have high-level requirement specifications that define security policies and include a set of access control policies. Taking advantage of this rich source of information, we introduce a top-down policy engineering framework for ABAC that aims to automatically extract policies from unrestricted natural language documents and then, we present our methodology to extract policy related information using deep neural networks. We first create an annotated dataset comprised of 2660 sentences from real-world policy documents. We then train a deep recurrent neural network (RNN) to identify sentences containing access control policies (ACP) from irrelevant content. We applied the RNN to our new dataset as well as to five other, smaller datasets that have been employed in prior work on this task, and show that our model outperforms the state-of-the-art and leads to a performance improvement of 5.58% over the previously reported results.

Role Based Access Control (RBAC) is the most widely used advanced access control model deployed in a variety of organizations. To deploy an RBAC system, one needs to first identify a complete set of roles, including permission role assignments and role user assignments. This process, known as role engineering, has been identified as one of the costliest tasks in migrating to RBAC. Since many organizations already have some form of user permission assignments defined, it makes sense to identify roles from this existing information. This process, known as role mining, has gained significant interest in recent years and numerous role mining techniques have been developed that take into account the characteristics of the core RBAC model, as well as its various extended features and each is based on a specific optimization metric. In this paper, we propose a generic approach which transforms the role mining problem into a constraint satisfaction problem. The transformation allows us to discover the optimal RBAC state based on customized optimization metrics. We also extend the RBAC model to include more context-aware and application specific constraints. These extensions broaden the applicability of the model beyond the classic role mining to include features such as permission usage, hierarchical role mining, hybrid role engineering approaches, and temporal RBAC models. We also perform experiments to show applicability and effectiveness of the proposed approach.

Role Based Access Control (RBAC) is the most widely used model for access control due to the ease of administration as well as economic benefits it provides. 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. In this paper, we propose a top-down role engineering approach and take the first steps towards using natural language processing techniques to extract policies from unrestricted natural language documents. Most organizations have high-level requirement specifications that include a set of access control policies which describes allowable operations for the system. However, it is very time consuming, labor-intensive, and error-prone to manually sift through these natural language documents to identify and extract access control policies. Our goal is to automate this process to reduce manual efforts and human errors. We apply natural language processing techniques, more specifically semantic role labeling to automatically extract access control policies from unrestricted natural language documents, define roles, and build an RBAC model. Our preliminary results are promising and by applying semantic role labeling to automatically identify predicate-argument structure, and a set of predefined rules on the extracted arguments, we were able correctly identify access control policies with a precision of 75%, recall of 88%, and F1 score of 80%.

Abstract Existing anti‐malware products usually use signature‐based techniques as their main detection engine. Although these methods are very fast, they are unable to provide effective protection against newly discovered malware or mutated variant of old malware. Heuristic approaches are the next generation of detection techniques to mitigate the problem. These approaches aim to improve the detection rate by extracting more behavioral characteristics of malware. Although these approaches cover the disadvantages of signature‐based techniques, they usually have a high false positive, and evasion is still possible from these approaches. In this paper, we propose an effective and efficient heuristic technique based on static analysis that not only detect malware with a very high accuracy, but also is robust against common evasion techniques such as junk injection and packing. Our proposed system is able to extract behavioral features from a unique structure in portable executable, which is called dynamic‐link library dependency tree, without actually executing the application. Copyright © 2015 John Wiley & Sons, Ltd.

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.

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.

With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstopp

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.

Considering the growth of wireless communication and mobile positioning technologies, location-based services (LBSs) have been generating increasing research interest in recent years. One of the critical issues for the deployment of LBS applications is how to reconcile their quality of service with

Role-based access control (RBAC) models are a powerful tool for describing and managing authorization, particularly, in large organizations. The benefits of using formal methods to describe RBAC models in a clear, consistent and rigorous manner have been recognized. Notable exemplars, that have been formulated in the formal specification notation Z, include NIST's reference RBAC model and the minimalist Core RBAC model. These models, however, do not support delegation, an important authorization feature which is often deployed in real access control systems. In RBAC, delegation empowers a user in a certain role to authorize another user to perform the tasks permissible to that role. This paper aims at integrating a version of role delegation, known as grant independent delegation, with the Core RBAC model. The paper introduces a state based model in which grant independent delegation and revocation operations are formally specified in Z. Integration with the Core RBAC model is achieved by simply combining the two models using the standard Z schema conjunction operator.

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.

Role-Based Access Control (RBAC) model is naturally suitable to organizations where users are assigned organizational roles with well-defined privileges. However, due to the large number of users in nowadays online services of organizations and enterprises, assigning users to roles is a tiresome task and maintaining user-role assignment up- to-date is costly and error-prone. Additionally, with the increasing number of users, RBAC may have problems in prohibiting cheat and changing roles of users. In order to categorize information and formulate security policies, human decision making is required which is naturally fuzzy in the real world. This leads using a fuzzy approach to address the issue in order to provide a more practical solution. In this paper, applicability of fuzzy set theory to RBAC has been investigated by identifying access control building blocks which are fuzzy in essence. An existing RBAC model is extended to allow imprecise access control policies, using the concept of trustworthiness which is fuzzy in nature. We call the extended model as Fuzzy RBAC. Applicability of the extended model has been evaluated through some case studies.

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.