Modern enterprise systems support Role-Based Access Control (RBAC). Although RBAC allows restricting access to privileged operations, a deployer may actually intend to restrict access to privileged data. This paper presents a theoretical foundation for correlating an operation-based RBAC policy with a data-based RBAC policy. Relying on a locationconsistency property, this paper shows how to infer whether an operation-based RBAC policy is equivalent to any databased RBAC policy. We have built a static analysis tool for Java Platform, Enterprise Edition (Java EE) called Static Analysis for Validation of Enterprise Security (SAVES). Relying on interprocedural pointer analysis and dataflow analysis, SAVES analyzes Java EE bytecode to determine if the associated RBAC policy is location consistent, and reports potential security flaws where location consistency does not hold. The experimental results obtained by using SAVES on a number of production-level Java EE codes have identified several security flaws with no false positive reports.
This work describes a new technique for analysis of Java 2, Enterprise Edition (J2EE) applications. In such applications, Enterprise Java Beans (EJBs) are commonly used to encapsulate the core computations performed on Web servers. Access to EJBs is protected by application servers, according to role-based access control policies that may be created either at development or deployment time. These policies may prohibit some types of users from accessing specific EJB methods.We present a static technique for analyzing J2EE access control policies with respect to security-sensitive fields of EJBs and other server-side objects. Our technique uses points-to analysis to determine which object fields are accessed by which EJB methods, directly or indirectly. Based on this information, J2EE access control policies are analyzed to identify potential inconsistencies that may lead to security holes.
In the security and privacy fields, Access Control (AC) systems are viewed as the fundamental aspects of networking security mechanisms. Enforcing AC becomes even more challenging when researchers and data analysts have to analyze complex and distributed Big Data (BD) processing cluster frameworks, which are adopted to manage yottabyte of unstructured sensitive data. For instance, Big Data systems' privacy and security restrictions are most likely to failure due to the malformed AC policy configurations. Furthermore, BD systems were initially developed toped to take care of some of the DB issues to address BD challenges and many of these dealt with the "three Vs" (Velocity, Volume, and Variety) attributes, without planning security consideration, which are considered to be patch work. Some of the BD "three Vs" characteristics, such as distributed computing, fragment, redundant data and node-to node communication, each with its own security challenges, complicate even more the applicability of AC in BD. This paper gives an overview of the latest security and privacy challenges in BD AC systems. Furthermore, it analyzes and compares some of the latest AC research frameworks to reduce privacy and security issues in distributed BD systems, which very few enforce AC in a cost-effective and in a timely manner. Moreover, this work discusses some of the future research methodologies and improvements for BD AC systems. This study is valuable asset for Artificial Intelligence (AI) researchers, DB developers and DB analysts who need the latest AC security and privacy research perspective before using and/or improving a current BD AC framework.
Mobile devices have revolutionized many aspects of our lives. We use smartphones and tablets as portable computers and, often without realizing it, we run various types of securitysensitive programs on them, such as personal and enterprise email and instant-messaging applications, as well as social, banking, insurance and retail programs. These applications access and transmit over the network numerous pieces of private information, including our geographical location, device ID, contacts, calendar events, passwords, and health records, as well as creditcard, social-security, and bank-account numbers. Guaranteeing that no private information is exposed to unauthorized observers is very challenging given the level of complexity that these applications have reached. Furthermore, using program-analysis tools with out-of-the-box configurations in order to detect confidentiality violations may not yield the desired results because only a few pieces of private data, such as the device's ID and geographical location, are obtained from standard sources. The majority of confidentiality sources (such as credit-card and bankaccount numbers) are application-specific and require careful configuration. This paper presents Labyrinth, a run-time privacy enforcement system that automatically detects leakage of private data originating from standard as well as application-specific sources. Labyrinth features several novel contributions: (i) it allows for visually configuring, directly atop the application's User Interface (UI), the fields that constitute custom sources of private data; (ii) it does not require operating-system instrumentation, but relies only an application-level instrumentation and on a proxy that intercepts the communication between the mobile device and the back-end servers; and (iii) it performs an enhanced form of valuesimilarity analysis to detect data leakage even when sensitive data (such as a password) has been encoded or hashed. Labyrinth supports both Android and iOS. We have evaluated Labyrinth experimentally, and in this paper we report results on productionlevel applications.
Given a large component-based program, it may be very complex to identify an optimal access-control policy, allowing the program to execute with no authorization failures and no violations of the Principle of Least Privilege. This paper presents a novel combination of static and dynamic analysis for automatic determination of precise accesscontrol policies for programs that will be executed on Stack-Based Access Control systems, such as Java and the Common Language Runtime (CLR). The static analysis soundly models the execution of the program taking into account native methods, reflection, and multi-threaded code. The dynamic analysis interactively refines the potentially conservative results of the static analysis, with no need for writing or generating test cases or for restarting the system if an authorization failure occurs during testing, and no risk of corrupting the underlying system on which the analysis is performed. We implemented the analysis framework presented by this paper in an analysis tool for Java programs, called Access-Control Explorer (ACE). ACE allows for automatic, safe, and precise identification of access-right requirements and library-code locations that should be made privilegeasserting to prevent client code from requiring unnecessary access rights. This paper presents experimental results obtained on large production-level applications. assertion requirements of the entire RCP. Part of the static analysis work described in this paper has been used to facilitate that goal.
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