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15th IEEE Int Conf on Trust, Security and Privacy in Comp and Commun / 10th IEEE Int Conf on Big data Science and Engineering / 14th IEEE Int symposium on Parallel and Distributed Proc with Applicat (IEEE Trustcom/BigdataSE/ISPA)

作者： Cao, Zhenhai Lang, Bo Wang, Jinmiao Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781509032051

Smart grid has a large number of users and numerous data analysis organizations. the privacy-preserving and access control for multidimensional power data have become vital security concerns. For correct aggregation of multidimensional data, existing schemes require transmitting attribute sets of data together with ciphertexts to the gateway (GW), which leaks the relevant information of power data and increases the security risk. Moreover, to implement access control of the multidimensional data, control center (CC) must firstly decrypt the data of all dimensions. It means CC can access all of the data without authorization, which may result in the data leakage. For coping with these problems, we propose an efficient and Fine-grained Access control scheme for Multidimensional data Aggregation, named FAMA. It uses the extended Boneh-goh-nissim homomorphic encryption (EBGN) to enforce multidimensional privacy-preserving data aggregation without transmitting attribute sets and attribute-based encryption (ABE) to implement fine-grained access control at dimension-level. By using FAMA, GW does not need to distinguish different ciphertexts. And every receiver can only extract the specific dimensions that it has been authorized, which not only restricts the privileges of CC, but also simplifies the operations of GW and CC. Moreover, after optimizing the scalar multiplication on elliptic curve, the encryption of FAMA is more efficient than the existing schemes.

关键词： smart grid multidimensional aggregation access control privacy-preserving EBGN KP-ABE

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Improved Scheduling Algorithms for Single-Path Multiple Bandwidth Reservation Requests 15

Improved Scheduling Algorithms for Single-Path Multiple Band...

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作者： Zuo, Liudong Zhu, Michelle Mengxia Calif State Univ Dominguez Hills Comp Sci Dept Carson CA 90747 USA Montclair State Univ Dept Comp Sci Montclair NJ 07043 USA

ISBN: (纸本)9781509032051

Colossal amounts of data are being generated in extreme-scale e-Sciences with the advent of new computation tools and experimental infrastructures. Such extremely large and complex data sets normally need to be transferred remotely for data storage and analysis. Reserving bandwidth as needed along selected paths in high-performance networks (HPNs) has proved to be an effective way to satisfy the high-demanding requirements of such data transfer. the most common data transfer requirement from users is the data transfer deadline. However, users oftentimes want to achieve other data transfer performance parameters, such as the earliest completion time (ECT) and the shortest duration (SD). For the bandwidth reservation service provider, all bandwidth reservation requests (BRRs) in one batch should be scheduled for high scheduling efficiency and system throughput. In this paper, we study the problem of scheduling all BRRs in one batch while achieving their best average transfer performance on one reservation path in an HPN. Two data transfer performance parameters, ECT and SD, are specifically considered. Because of the limited bandwidth resources of the reservation path, the problems of scheduling all BRRs in one batch on one reservation path while achieving their best average ECT and SD are converted into the problems of scheduling as many BRRs as possible while achieving the average ECT and SD of scheduled BRRs, respectively. We prove these two converted problems as NP-complete problems, and improve two existing heuristic algorithms proposed previously for similar problems. Extensive simulation experiments show the superior scheduling performance of these improved algorithms in terms of several performance metrics.

关键词： Bandwidth reservation bandwidth scheduling high-performance networks big data QoS

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Learning a Strategy for Choosing Widening thresholds from a large Codebase 14th

Learning a Strategy for Choosing Widening Thresholds from a ...

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14th Asian symposium on Programming Languages and Systems (APLAS)

作者： Cha, Sooyoung Jeong, Sehun Oh, Hakjoo Korea Univ Seoul South Korea

ISBN: (纸本)9783319479576;9783319479583

In numerical static analysis, the technique of widening thresholds is essential for improving the analysis precision, but blind uses of the technique often significantly slow down the analysis. Ideally, an analysis should apply the technique only when it benefits, by carefully choosing thresholds that contribute to the final precision. However, finding the proper widening thresholds is nontrivial and existing syntactic heuristics often produce suboptimal results. In this paper, we present a method that automatically learns a good strategy for choosing widening thresholds from a given codebase. A notable feature of our method is that a good strategy can be learned with analyzing each program in the codebase only once, which allows to use a large codebase as training data. We evaluated our technique with a static analyzer for full C and 100 open-source benchmarks. the experimental results show that the learned widening strategy is highly cost-effective;it achieves 84% of the full precision while increasing the baseline analysis cost only by 1.4x. Our learning algorithm is able to achieve this performance 26 times faster than the previous Bayesian optimization approach.

关键词： Static analysis

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A Hadoop based analysis and detection model for IP Spoofing typed DDoS attack 15

A Hadoop based analysis and detection model for IP Spoofing ...

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作者： Zhang, Jian Liu, Pin He, Jianbiao Zhang, Yawei Cent S Univ Trusted Comp Inst Changsha 410083 Hunan Peoples R China Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

ISBN: (纸本)9781509032051

As more and more cloud services are exposed to DDoS attacks, DDoS attack detect has become a new challenging task because large packet traces captured on fast links could not be easily handled on a single server with limited computing and memory resources. In this paper, we propose a Hadoop based model to identify abnormal packets and compute the statistics according to the number of abnormal packets. the novelties of the model are that:(1) by harnessing HBASE, an improved bloom filter based mapping mechanism named TCP2HC/UDP2HC are implemented;(2) with the characteristics of IP spoofing and the temporal correlation of transport layer connection state, an extensible set of rules and a reliable MapReduce based checking mechanism for abnormal packets are designed;(3) using statistic features extracted from the increased abnormal packets and TCP/UDP flow, a non-parameter CUSUM algorithm is used to detect most DDoS attacks accurately and efficiently. the model can detect the attack behavior in the early stage, which is beneficial to mitigate attack with the help of flow cleaning by converting a check rule to the filtering rule. Experiments show no matter how large the attack scale and what kind of DDoS attack, the detection model can soon detect DDoS attack accurately.

关键词： DDoS Hadoop MapReduce HBASE Cloud CUSUM

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Sensitivity of shoreline-recession forecasts to sediment budget uncertainties

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JOURNAL OF COASTAL RESEARCH 2016年第75期000卷 947-951页

作者： de Figueiredo, Salette A. Cowell, Peter J. Univ Fed Rio Grande Inst Oceanog Rio Grande Brazil Univ Sydney Coastal Studies Unit Sydney NSW 2006 Australia

Intrinsic uncertainty associated with the stochastic nature of coastal processes and how they affect sediment budget estimates under present conditions already make predicting coastal change difficult. the possibility that under climate change conditions alteration of wave climate will have direct implications for alongshore transport in future, may complicate even more future forecasts of coastal change in response to sea-level rise. this study exploits the variability in sediment budgets along the Rio Grande do Sul coastal plain, in southern Brazil, to provide an experimental framework to evaluate the significance of such uncertainties in coastal response under the rising sea levels rates predicted for the next century. the analysis involved quantifying the variation in coastal recession sensitivity between different sectors along the coast (coastal cells), through the application of RanSTM (Random Shoreface Translation Model) using two different scenarios for the sediment-budget parameter. Sediment budget parameter was estimated based on historical shoreline changes and Holocene barrier stratigraphic data. the results show that forecasts for lower-magnitude coastal recession associated with more likely impacts (e.g. 90% risk) are more susceptible to uncertainties in littoral sediment budgets than high-magnitude less likely impacts (e.g. 10% risk). this is especially the case for coastal cells with low dune dimensions because in general such dunes have less capacity to buffer coastal-erosion effects. the reduced effect on high-end forecasts with respect to sediment budget uncertainties therefore favors better confidence in the forecasts.

关键词： Sea level rise large scale processes coastal response

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FOG-Engine: Towards Big data Analytics in the Fog

FOG-Engine: Towards Big Data Analytics in the Fog

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IEEE International symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Farahd Mehdipour Bahman Javadi Aniket Mahanti Tech Futures Lab & Unitec Institute of Technology Auckland New Zealand Western Sydney University Sydney Australia University of Auckland Auckland New Zealand

Existing platforms fall short in providing effective solutions for big data analytics while the demands for processing large quantities of data in real-time are increasing. Moving data analytics towards where the data is generated and stored could be a solution for addressing this issue. In this paper, we propose a solution referred as FOG-engine, which is integrated into IoTs near the ground and facilitates data analytics before offloading large amounts of data to a central location. In this work, we introduce a model for data analytic using FOG-engines and discuss our plan for evaluating its efficacy in terms of several performance metrics such as processing speed, network bandwidth, and data transfer size.

关键词： fogs fog Performance metrics central location data analysis NEBULISED Solutions big data network bandwidth UNLOADING data transmission PROCESSING SPEED ground large QUANTITIES

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Filter large-scale engine data using apache spark

Filter large-scale engine data using apache spark

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Donato Pirozzi Vittorio Scarano Steven Begg Guillaume De Sercey Andrew Fish Andrew Harvey Dipartimento di Informatica Università degli Studi di Salerno IT School of Computing Engineering and Mathematics University of Brighton UK

ISBN: (纸本)9781509028719

this paper introduces a minimum viable software product to filter large datasets of engine data recorded during laboratory experiments of combustion engines. the aim is to support analysts in the identification and analysis of specific physical phenomenon within hours of recorded engine experimental data. Specifically, the tool has been designed considering the use case of identifying Low Speed Pre-Ignition events. this work describes the tool's graphical user interface and its scalable architecture based on mainstream web and big-data technologies as well as the practical application to pre-ignition events identification. the paper provides details on the architecture's performance, providing evidence of its scalability by increasing the number of available computing workers.

关键词： Engines Combustion Graphical user interfaces data visualization Sparks Bars Industries

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Interactive Healthcare Big data Analytics Platform under Simulated Performance

Interactive Healthcare Big Data Analytics Platform under Sim...

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IEEE International symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Dillon Chrimes Belaid Moa Hamid Zamani Mu-Hsing Kuo Vancouver Island Health Authority University of Victoria Victoria BC Canada Compute Canada/WestGrid University of Victoria Victoria BC Canada School of Health Information Science University of Victoria Victoria BC Canada School of Health Information Science Stanford University BC USA

To utilize data from hospital systems, big data analytics (BDA) has become increasingly important. BDA enable queries of large highly diverse and real volumes of patient data in an interactively dynamic way that enriches the use of the platform with data visualization for healthcare. We established a Healthcare BDA (HBDA) platform at the University of Victoria (UVic) with Compute Canada/Westgrid, and Vancouver Island Health Authority (VIHA), Victoria, BC, Canada. the framework was a proof-of-concept implementation that tested emulated patient data representative of the main hospital system at VIHA. We cross-referenced all data, its profiles and metadata, with the existing clinical reporting. Our HBDA platform and its performance was tested for different patient query types in simulation with the data ingested into Hadoop file system over different applications of Apache Spark with Zeppelin and Jupyter web-based interfaces, and Apache Drill interfaces. the results showed that the ingestion time of one billion records took circa 2 hours via Apache Spark. Apache Drill outperformed Spark/Zeppelin and Spark/Jupyter. However, it was restricted to running more simplified queries, and very limited in its visualizations exhibiting poor usability for healthcare. Zeppelin running on Spark showed ease-of-use interactions for health applications, but it lacked the flexibility of its interface tools and required extra setup time before running queries. Jupyter on Spark offered high performance stacks not only over our HBDA platform but also in unison to run all queries simultaneously with high usability for a variety of reporting requirements by providers and health professionals.

关键词： Big data Hospitals databases data visualization Metadata

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Enhancing mobile device security with process mining

Enhancing mobile device security with process mining

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International symposium on Intelligent Systems and Informatics (SISY)

作者： Ladislav Hluchý Ondrej Habala Institute of Informatics Slovak Academy of Sciences Bratislava Slovakia

We present part of a research project aimed at mobile device security. During the project we have tested several methods of processing of data from mobile devices in order to detect attacks and suspicious activity in general. One method we have tested was also mining of processes found in mobile device activity logs and analysis of those processes. We have used several devices with the Android TM OS, collected a large amount of activity data, including (simulated) harmful activity, and then modeled processes found in the data. Results of the comparison of these processes to the harmful activity suggest that this application of process mining shows promise, but also requires a lot of further research.

关键词： data mining Mobile handsets Security Performance evaluation Business Petri nets Browsers

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Better IT services by means of data mining

Better IT services by means of data mining

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International symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： M. Vadovský P. Michalik I. Zolotová J. Paralič Department of Cybernetics and Artificial Intelligence Technical University of Košice Košice Slovakia

this article contains description how to create prediction models for more efficient utilization of running IT resources. Prediction model creation is presented as one of the most important steps within the process of the knowledge discovery. Presented models were created based on real data from a local IT company by means of the programming language R. this article also marginally covers the steps, which are to be carried out before the actual modelling. the steps to mention are the following: available data understanding and their subsequent preprocessing for further work, individual attributes analysis using data visualization by means of graphs and tracking certain behavior and correlations in data. First of all, designing models using linear and multiple regression will be followed in detail. then, the methods of new values prediction will be described using the R language. Finally, the different types of prediction models will be considered using appropriate indicators. the results obtained should assist and simplify the methods of the knowledge discovery in certain enterprises based on the available databases, and also help to choose the most suitable prediction models learning method.

关键词： Predictive models Biological system modeling data models Correlation Knowledge discovery data mining Servers

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