Big Data Processing Attribute Based Access Control Security

The purpose of this research is to analyze the security of next-generation big data processing (BDP) and examine the feasibility of applying advanced security features to meet the needs of modern multi-tenant, multi-level data analysis. The research methodology was to survey of the status of security mechanisms in BDP systems and identify areas that require further improvement. Access control (AC) security services were identified as priority area, specifically Attribute Based Access Control (ABAC). The exemplar BDP system analyzed is the Apache Hadoop ecosystem. We created data generation software, analysis programs, and posted the detailed the experiment configuration on GitHub. Overall, our research indicates that before a BDP system, such as Hadoop, can be used in operational environment significant security configurations are required. We believe that the tools are available to achieve a secure system, with ABAC, using Apache Ranger and Apache Atlas. However, these systems are immature and require verification by an independent third party. We identified the following specific actions for overall improvement: consistent provisioning of security services through a data analyst workstation, a common backplane of security services, and a management console. These areas are partially satisfied in the current Hadoop ecosystem, continued AC improvements through the open source community, and rigorous independent testing should further address remaining security challenges. Robust security will enable further use of distributed, cluster BDP, such as Apache Hadoop and Hadoop-like systems, to meet future government and business requirements

Enhancing data privacy and security in Internet of Things through decentralized models and services

exploits a Byzantine Fault Tolerant (BFT) blockchain, in order to perform collaborative and dynamic botnet detection by collecting and auditing IoT devices\u2019 network traffic flows as blockchain transactions. Secondly, we take the challenge to decentralize IoT, and design a hybrid blockchain architecture for IoT, by proposing Hybrid-IoT. In Hybrid-IoT, subgroups of IoT devices form PoW blockchains, referred to as PoW sub-blockchains. Connection among the PoW sub-blockchains employs a BFT inter-connector framework. We focus on the PoW sub-blockchains formation, guided by a set of guidelines based on a set of dimensions, metrics and bounds

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)