18,678 research outputs found
A pattern-driven framework for monitoring security and dependability
In this paper we describe a framework that supports the dynamic configuration, adaptation and monitoring of systems that need to guarantee specific security and dependability (S&D) properties whilst operating in distributed settings. The framework is based on patterns providing abstract specifications of implementation solutions that can be used by systems in order to achieve specific S&D properties. The focus herein will be on the monitoring aspects of the framework which allow it to adapt to violations of the S&D requirements and changes to the current context
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Pattern-driven security, privacy, dependability and interoperability management of iot environments
Achieving Security, Privacy, Dependability and Interoperability (SPDI) is of paramount importance for the ubiquitous deployment and impact maximization of Internet of Things (IoT) applications. Nevertheless, said requirements are not only difficult to achieve at system initialization, but also hard to prove and maintain at run-time. This paper highlights an approach to tackling the above challenges, through the definition of pattern language and a framework that can guarantee SPDI in IoT orchestrations. By integrating pattern reasoning engines at the various layers of the IoT infrastructure, and a machine-processable representation of said pattern through Drools rules, the proposed framework can provide ways to fulfill SPDI requirements at design time, and also provide the means to guarantee those SPDI properties and manage the orchestrations accordingly. Moreover, an application example of the framework is presented in an Industrial IoT monitoring environment
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Towards a Security, Privacy, Dependability, Interoperability Framework for the Internet of Things
A popular application of ambient intelligence systems constitutes of assisting living services on smart buildings. As intelligence is imported in embedded equipment, the system becomes able to provide smart services (e.g. control lights, airconditioning, provide energy management services etc.). IoT is the main enabler of such environments. However, the interconnection of these cyber-physical systems and the processing of personal data raise serious security and privacy issues. In this paper we present a framework that can guarantee Security, Privacy, Dependability and Interoperability (SPDI) in IoT. Taking advantage of the underlying IoT deployment, the proposed framework not only implements the requested smart functionality but also provide modelling and administration that can guarantee those SPDI properties. Moreover, we provide an application example of the framework in a smart building scenario
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Detection of Security and Dependability Threats: A Belief Based Reasoning Approach
Monitoring the preservation of security and dependability (S&D) properties during the operation of systems at runtime is an important verification measure that can increase system resilience. However it does not always provide sufficient scope for taking control actions against violations as it only detects problems after they occur. In this paper, we describe a proactive monitoring approach that detects potential violations of S&D properties, called ldquothreatsrdquo, and discuss the results of an initial evaluation of it
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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