Geo-social-rbac: A location-based socially aware access control framework

The ubiquity of low-cost GPS-enabled mobile devices and the proliferation of online social networks have enabled the collection of rich geo-social information that includes the whereabouts of the users and their social connections. This information can be used to provide a rich set of access control policies that ensure that resources are utilized securely. Existing literature focuses on providing access control systems that control the access solely based on either the location of the users or their social connections. In this paper, we argue that a number of real-world applications demand an access control model that effectively captures both the geographic as well as the social dimensions of the users in a given location. We propose, Geo-social-RBAC, a new role based access control model that allows the inclusion of geo-social constraints as part of the access control policy. Our model, besides capturing the locations of a user requesting access and her social connections, includes geo-social cardinality constraints that dictate how many people related by a particular social relation need to be present in the required locations at the time of an access. The model also allows specification of geo-social and location trace constraints that may be used to dictate if an access needs to be granted or denied

Enterprise Information Security Management Based on Context-Aware RBAC and Communication Monitoring Technology

Information technology has an enormous influence in many enterprises. Computers have not only become important devices that people rely on in their daily lives and work, but have also become essential tools for enterprises. More and more enterprises have shifted their focus to how to prevent outer forces from invading and stealing from networks. However, many enterprises have disregarded the significance of internal leaking, which also plays a vital role in information management. This research proposes an information security management approach that is based on context-aware role-based access control (RBAC) and communication monitoring technology, in order to achieve enterprise information security management. In this work, it is suggested that an enterprise may, first, use an organizational chart to list job roles and corresponding permissions. RBAC is a model that focuses on different work tasks and duties. Subsequently, the enterprise may define a security policy to enforce the context-aware RBAC model. Finally, the enterprise may use communication monitoring technology in order to implement information security management. The main contribution of this work is the potential it provides to both reduce information security incidents, such as internal information leakage, and allow for effective cost control of information systems

A Context-Aware System to Secure Enterprise Content: Incorporating Reliability Specifiers

The sensors of a context-aware system extract contextual information from the environment and relay that information to higher-level processes of the system so to influence the system\u2019s control decisions. However, an adversary can maliciously influence such controls indirectly by manipulating the environment in which the sensors are monitoring, thereby granting privileges the adversary would otherwise not normally have. To address such context monitoring issues, we extend CASSEC by incorporating sentience-like constructs, which enable the emulation of \u201dconfidence\u201d, into our proximity-based access control model to grant the system the ability to make more inferable decisions based on the degree of reliability of extracted contextual information. In CASSEC 2.0, we evaluate our confidence constructs by implementing two new authentication mechanisms. Co-proximity authentication employs our time-based challenge-response protocol, which leverages Bluetooth Low Energy beacons as its underlying occupancy detection technology. Biometric authentication relies on the accelerometer and fingerprint sensors to measure behavioral and physiological user features to prevent unauthorized users from using an authorized user\u2019s device. We provide a feasibility study demonstrating how confidence constructs can improve the decision engine of context-aware access control systems

Applications of Context-Aware Systems in Enterprise Environments

In bring-your-own-device (BYOD) and corporate-owned, personally enabled (COPE) scenarios, employees’ devices store both enterprise and personal data, and have the ability to remotely access a secure enterprise network. While mobile devices enable users to access such resources in a pervasive manner, it also increases the risk of breaches for sensitive enterprise data as users may access the resources under insecure circumstances. That is, access authorizations may depend on the context in which the resources are accessed. In both scenarios, it is vital that the security of accessible enterprise content is preserved. In this work, we explore the use of contextual information to influence access control decisions within context-aware systems to ensure the security of sensitive enterprise data. We propose several context-aware systems that rely on a system of sensors in order to automatically adapt access to resources based on the security of users’ contexts. We investigate various types of mobile devices with varying embedded sensors, and leverage these technologies to extract contextual information from the environment. As a direct consequence, the technologies utilized determine the types of contextual access control policies that the context-aware systems are able to support and enforce. Specifically, the work proposes the use of devices pervaded in enterprise environments such as smartphones or WiFi access points to authenticate user positional information within indoor environments as well as user identities

Secure platforms for enforcing contextual access control

Advances in technology and wide scale deployment of networking enabled portable devices such as smartphones has made it possible to provide pervasive access to sensitive data to authorized individuals from any location. While this has certainly made data more accessible, it has also increased the risk of data theft as the data may be accessed from potentially unsafe locations in the presence of untrusted parties. The smartphones come with various embedded sensors that can provide rich contextual information such as sensing the presence of other users in a context. Frequent context profiling can also allow a mobile device to learn its surroundings and infer the familiarity and safety of a context. This can be used to further strengthen the access control policies enforced on a mobile device. Incorporating contextual factors into access control decisions requires that one must be able to trust the information provided by these context sensors. This requires that the underlying operating system and hardware be well protected against attacks from malicious adversaries. ^ In this work, we explore how contextual factors can be leveraged to infer the safety of a context. We use a context profiling technique to gradually learn a context\u27s profile, infer its familiarity and safety and then use this information in the enforcement of contextual access policies. While intuitive security configurations may be suitable for non-critical applications, other security-critical applications require a more rigorous definition and enforcement of contextual policies. We thus propose a formal model for proximity that allows one to define whether two users are in proximity in a given context and then extend the traditional RBAC model by incorporating these proximity constraints. Trusted enforcement of contextual access control requires that the underlying platform be secured against various attacks such as code reuse attacks. To mitigate these attacks, we propose a binary diversification approach that randomizes the target executable with every run. We also propose a defense framework based on control flow analysis that detects, diagnoses and responds to code reuse attacks in real time

TACKLING INSIDER THREATS USING RISK-AND-TRUST AWARE ACCESS CONTROL APPROACHES

Insider Attacks are one of the most dangerous threats organizations face today. An insider attack occurs when a person authorized to perform certain actions in an organization decides to abuse the trust, and harm the organization by causing breaches in the confidentiality, integrity or availability of the organization’s assets. These attacks may negatively impact the reputation of the organization, its productivity, and may incur heavy losses in revenue and clients. Preventing insider attacks is a daunting task. Employees need legitimate access to effectively perform their jobs; however, at any point of time they may misuse their privileges accidentally or intentionally. Hence, it is necessary to develop a system capable of finding a middle ground where the necessary privileges are provided and insider threats are mitigated. In this dissertation, we address this critical issue. We propose three adaptive risk-and-trust aware access control frameworks that aim at thwarting insider attacks by incorporating the behavior of users in the access control decision process. Our first framework is tailored towards general insider threat prevention in role-based access control systems. As part of this framework, we propose methodologies to specify risk-and-trust aware access control policies and a risk management approach that minimizes the risk exposure for each access request. Our second framework is designed to mitigate the risk of obligation-based systems which are difficult to manage and are particularly vulnerable to sabotage. As part of our obligation-based framework, we propose an insider-threat-resistant trust computation methodology. We emphasize the use of monitoring of obligation fulfillment patterns to determine some psychological precursors that have high predictive power with respect to potential insider threats. Our third framework is designed to take advantage of geo-social information to deter insider threats. We uncover some insider threats that arise when geo-social information is used to make access control decisions. Based on this analysis, we define an insider threat resilient access control approach to manage privileges that considers geo-social context. The models and methodologies presented in this dissertation can help a broad range of organizations in mitigating insider threats

A formal proximity model for RBAC systems

To combat the threat of information leakage through pervasive access, researchers have proposed several extensions to the popular role-based access control (RBAC) model. Such extensions can incorporate contextual features, such as location, into the policy decision in an attempt to restrict access to trustworthy settings. In many cases, though, such extensions fail to reflect the true threat, which is the presence or absence of other users, rather than absolute locations. For instance, for location-aware separation of duty, it is more important to ensure that two people are in the same room, rather than in a designated, pre-defined location. Prox-RBAC was proposed as an extension to consider the relative proximity of other users with the help of a pervasive monitoring infrastructure. However, that work offered only an informal view of proximity, and unnecessarily restricted the domain to spatial concerns. In this work, we present a more rigorous definition of proximity based on formal topological relations. In addition, we show that this definition can be applied to several additional domains, such as social networks, communication channels, attributes, and time; thus, our policy model and language is more flexible and powerful than the previous work. In addition to proposing the model, we present a number of theoretical results for such systems, including a complexity analysis, templates for cryptographic protocols, and proofs of security features