205,737 research outputs found

    Access Control for IoT: Problems and Solutions in the Smart Home

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    The Internet of Things (IoT) is receiving considerable amount of attention from both industry and academia due to the business models that it enables and the radical changes it introduced in the way people interact with technology. The widespread adaption of IoT in our everyday life generates new security and privacy challenges. In this thesis, we focus on "access control in IoT": one of the key security services that ensures the correct functioning of the entire IoT system. We highlight the key differences with access control in traditional systems (such as databases, operating systems, or web services) and describe a set of requirements that any access control system for IoT should fulfill. We demonstrate that the requirements are adaptable to a wide range of IoT use case scenarios by validating the requirements for access control elicited when analyzing the smart lock system as sample use case from smart home scenario. We also utilize the CAP theorem for reasoning about access control systems designed for the IoT. We introduce MQTT Security Assistant (MQTTSA), a tool that automatically detects misconfigurations in MQTT-based IoT deployments. To assist IoT system developers, MQTTSA produces a report outlining detected vulnerabilities, together with (high level) hints and code snippets to implement adequate mitigations. The effectiveness of the tool is assessed by a thorough experimental evaluation. Then, we propose a lazy approach to Access Control as a Service (ACaaS) that allows the specification and management of policies independently of the Cloud Service Providers (CSPs) while leveraging its enforcement mechanisms. We demonstrate the approach by investigating (also experimentally) alternative deployments in the IoT platform offered by Amazon Web Services on a realistic smart lock solution

    Towards Model-Driven Development of Access Control Policies for Web Applications

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    We introduce a UML-based notation for graphically modeling systems’ security aspects in a simple and intuitive way and a model-driven process that transforms graphical specifications of access control policies in XACML. These XACML policies are then translated in FACPL, a policy language with a formal semantics, and the resulting policies are evaluated by means of a Java-based software tool

    Securing library information system: Vulnerabilities and threats

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    Threats and vulnerabilities in computers and networks are common nowadays since computers are widely used by the public. The risks of computer threats and vulnerabilities are high since most computers are connected to the internet. Library Information Systems is also vulnerable to attack since it is a public access institution. Majority of users are naive when it comes to computer and network securities. Some breaches in Library Information System are intentional and some are unintentional. Risks analysis should be done to find the threats and risks in designing the Library Information System. Threats are made possible due to lack of proper procedures, software flaws and policies. The administrators should anticipate all the possible attacks and their mitigation techniques. In this paper, we will try to address various issues arise from this vulnerabilities and threats. We will also describe how we can reduce and overcome this vulnerabilities and threats

    A Relational Hyperlink Analysis of an Online Social Movement

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    In this paper we propose relational hyperlink analysis (RHA) as a distinct approach for empirical social science research into hyperlink networks on the World Wide Web. We demonstrate this approach, which employs the ideas and techniques of social network analysis (in particular, exponential random graph modeling), in a study of the hyperlinking behaviors of Australian asylum advocacy groups. We show that compared with the commonly-used hyperlink counts regression approach, relational hyperlink analysis can lead to fundamentally different conclusions about the social processes underpinning hyperlinking behavior. In particular, in trying to understand why social ties are formed, counts regressions may over-estimate the role of actor attributes in the formation of hyperlinks when endogenous, purely structural network effects are not taken into account. Our analysis involves an innovative joint use of two software programs: VOSON, for the automated retrieval and processing of considerable quantities of hyperlink data, and LPNet, for the statistical modeling of social network data. Together, VOSON and LPNet enable new and unique research into social networks in the online world, and our paper highlights the importance of complementary research tools for social science research into the web

    Enabling Interactive Analytics of Secure Data using Cloud Kotta

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    Research, especially in the social sciences and humanities, is increasingly reliant on the application of data science methods to analyze large amounts of (often private) data. Secure data enclaves provide a solution for managing and analyzing private data. However, such enclaves do not readily support discovery science---a form of exploratory or interactive analysis by which researchers execute a range of (sometimes large) analyses in an iterative and collaborative manner. The batch computing model offered by many data enclaves is well suited to executing large compute tasks; however it is far from ideal for day-to-day discovery science. As researchers must submit jobs to queues and wait for results, the high latencies inherent in queue-based, batch computing systems hinder interactive analysis. In this paper we describe how we have augmented the Cloud Kotta secure data enclave to support collaborative and interactive analysis of sensitive data. Our model uses Jupyter notebooks as a flexible analysis environment and Python language constructs to support the execution of arbitrary functions on private data within this secure framework.Comment: To appear in Proceedings of Workshop on Scientific Cloud Computing, Washington, DC USA, June 2017 (ScienceCloud 2017), 7 page

    A Semantic Framework for the Analysis of Privacy Policies

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