Reputation-based secyrity for heterogtneous structurally complex systems
In this paper we present the service-oriented infrastructure within the Wide Area Grid (WAG) project that was carried out within the Working Group on Information Systems and Services of the Committee on Earth Observation Satellites (CEOS). The study focuses on enabling trust for this infrastructure...
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pp_isofts_kiev_ua-article-922018-09-20T12:57:00Z Reputation-based secyrity for heterogtneous structurally complex systems Kussul, O.M. Іnformation security system UDC 004.056 In this paper we present the service-oriented infrastructure within the Wide Area Grid (WAG) project that was carried out within the Working Group on Information Systems and Services of the Committee on Earth Observation Satellites (CEOS). The study focuses on enabling trust for this infrastructure using certificates and reputation-based model. Інститут програмних систем НАН України 2015-09-10 Article Article application/pdf https://pp.isofts.kiev.ua/index.php/ojs1/article/view/92 PROBLEMS IN PROGRAMMING; No 2-3 (2012) ПРОБЛЕМЫ ПРОГРАММИРОВАНИЯ; No 2-3 (2012) ПРОБЛЕМИ ПРОГРАМУВАННЯ; No 2-3 (2012) 1727-4907 uk https://pp.isofts.kiev.ua/index.php/ojs1/article/view/92/91 Copyright (c) 2015 ПРОБЛЕМИ ПРОГРАМУВАННЯ |
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Іnformation security system UDC 004.056 Kussul, O.M. Reputation-based secyrity for heterogtneous structurally complex systems |
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Reputation-based secyrity for heterogtneous structurally complex systems |
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Reputation-based secyrity for heterogtneous structurally complex systems |
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Reputation-based secyrity for heterogtneous structurally complex systems |
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Reputation-based secyrity for heterogtneous structurally complex systems |
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Reputation-based secyrity for heterogtneous structurally complex systems |
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reputation-based secyrity for heterogtneous structurally complex systems |
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In this paper we present the service-oriented infrastructure within the Wide Area Grid (WAG) project that was carried out within the Working Group on Information Systems and Services of the Committee on Earth Observation Satellites (CEOS). The study focuses on enabling trust for this infrastructure using certificates and reputation-based model. |
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Інститут програмних систем НАН України |
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2015 |
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Захист інформації
UDC 004.056
REPUTATION-BASED SECURITY FOR HETEROGENEOUS
STRUCTURALLY COMPLEX SYSTEMS
O.M. Kussul
National Technical University of Ukraine “Kyiv Polytechnic Institute”,
Institute of Physics and Technology, 37 Prospect Peremogy,
Kyiv 03056, Ukraine, olgakussul@gmail.com
In this paper we present the service-oriented infrastructure within the Wide Area Grid (WAG) project that was carried out within the
Working Group on Information Systems and Services of the Committee on Earth Observation Satellites (CEOS). The study focuses on
enabling trust for this infrastructure using certificates and reputation-based model.
В даній роботі розглянуто сервіс-орієнтовану інфраструктуру обробки супутникових даних, яка розроблена в межах
міжнародного проекту Wide Area Grid (WAG) робочої групи WGISS комітету супутникових спостережень CEOS. Розглянуто
питання забезпечення довіри в таких структурно-складних системах, зокрема на основі сертифікатів та репутації.
Introduction
At present, the solution of applied problems in many domains such as climate change, environment monitoring,
disaster management is impossible without extensive use of Earth observation (EO) data. A system of systems (SoS) is
required in order to enable solution of these problems at both regional and global scale. Such an approach envisages the
integrated use of satellite data and corresponding products and services, and integration of existing regional and
international satellite monitoring systems. It is particularly important for the area of disaster management where
different space agencies collaborate in order to timely provide necessary data and services in case of emergency.
A considerable need therefore exists for an appropriate infrastructure that will enable the integrated and
operational use of multi-source data for different applications domain. From technological point of view, Grids can
provide solutions to the above-mentioned problems. In this case, a Grid environment can be considered not only for
providing high-performance computations, but, in fact, can facilitate interactions between different actors by providing
a standard infrastructure and a collaborative framework to share data, algorithms, storage resources, and processing
capabilities [1–7].
There have been several studies on evaluating and using Grids for the EO domain. In particular, we may refer to
the GENESI-DR project (www.genesi-dr.eu) that aims at building a grid-based infrastructure supporting the Global
Earth Observation System of Systems (GEOSS) architecture. The European Space Agency (ESA) and the European
Space Research Institute (ESRIN) developed the Earth Observation Grid Processing on-Demand infrastructure (G-
POD) [4, 6]. FAIRE is another Grid-based application that is operationally used by ESA in the context of flood
mapping. The application takes advantage of Grid technology for near real-time (NRT) data access, calibration,
orthorectification, map projection, co-registration. The application is operationally used in the context of the
International Charter “Space and Major Events” (http://www.disasterscharter.org). DEGREE (Dissemination and
Exploitation of GRids in Earth science) project was a European-funded project that aimed to build a bridge linking the
Earth Science and Grid communities throughout Europe. Grid was considered to be the appropriate platform for
integration of heterogeneous data resources, processing tools, models, algorithms, and so on. The following applied
problems were within the scope of DEGREE: earthquake analysis, floods modelling and forecasting, and influence of
climate changes on agriculture.
In this paper we present the service-oriented infrastructure within the Wide Area Grid (WAG) project that was
carried out within the Working Group on Information Systems and Services (WGISS, http://www.ceos.org/wgiss) of the
Committee on Earth Observation Satellites (CEOS). The study focuses on enabling trust for this infrastructure using
certificates and reputation-based model.
1. The WAG project
The WAG project was initiated by the French Space Agency (CNES) within the CEOS WGISS activities with
main contributions from Space Research Institute (SRI) NASU-NSAU (Ukraine) and CEODE-CAS (China). The
project objective was to develop the “horizontal” infrastructure in order to integrate computational, human, intellectual,
and informational resources of the space agencies within a distributed system. In order to create such an infrastructure
Grid technologies were used. An application that was tackled within the project is flood monitoring and prediction.
Within the infrastructure services were developed in order to enable fast acquisition, processing and delivery of geo-
information products based on optical and synthetic-aperture radar (SAR) satellite data in case of emergency. The WAG
project is considered as CEOS WGISS contribution to GEOSS. Description of the infrastructure, technologies used and
case-study applications are given in details in the following sections.
©О.M. Kussul, 2012
ISSN 1727-4907. Проблеми програмування. 2012. № 2-3. Спеціальний випуск 374
Захист інформації
2. Service-oriented system for satellite data processing
Overall architecture. To enable automatic processing of satellite data, a service-oriented infrastructure was
implemented (Fig. 1). We followed the approach used in the Earth System Grid [8]. The four major components of the
system are as follows:
1. Client applications. Web portal is a main entry point, and provides interfaces to communicate with system
services.
2. High-level services. This level includes security subsystem including services for managing reputation,
catalogue services, metadata services (description and access), automatic workflow generation services, and data
aggregation, sub-setting & visualisation services. These services are connected to the Grid services at the lower level.
3. Grid services. These services provide access to the shared resources of the Grid system, access to credentials,
file transfer, job submission and management. The Grid services include: Globus Security Infrastructure (GSI),
GridFTP, Grid Resource Allocation Management (GRAM), MyProxy, Teplica Location Service (RLS), and OGSA-
DAI.
4. Database and application services. This level provides physical data and computational resources of the
system.
Fig. 1. Overall architecture of service-oriented structurally complex system for satellite data processing
The WAG Services. Using the described approach we implemented a service-oriented system for disaster
monitoring using satellite data. The system integrates geographically distributed resources of the SRI NASU-NSAU
(Ukraine), Institute of Cybernetics of NASU (IC NASU, Ukraine) and CEODE-CAS (China) [1-3, 5]. The system is
applied for disaster monitoring and management, in particular floods. In order to benefit from data of different nature
(for example, optical and radar) and provide integration of different products in case of emergency, SRI NASU-NSAU
flood mapping service was integrated with the flood mapping services provided by the CEODE-CAS. SRI NASU-
NSAU flood mapping service uses radar satellite data (Envisat/ASAR, Radarsat-2) while CEODE-CAS service is based
on the use of optical data acquired by MODIS instrument onboard Terra and Aqua satellites. Figure 2 shows the
architecture of the China-Ukrainian service-oriented system for disaster management.
The following Service Level Agreement (SLA) was delivered:
— geospatial products should be available within 24 hours after satellite image acquisition;
— spatial resolution of satellite images and corresponding products should be no less than 250 m.
The integration of the Ukrainian and Chinese systems is done at the level of services. The portals of SRI NASU-
NSAU and CEODE are operated independently and communicate with corresponding brokers that provide interfaces to
the flood mapping services. These brokers process requests from both local and trusted remote sites. For example, to
provide a flood mapping product using SAR data the CEODE portal generates a corresponding search request to the
broker at the SRI NASU-NSAU side based on user search parameters. This request is processed by the broker and the
search results are displayed at the CEODE portal. The user selects the SAR image file to be processed, and the request
is submitted to the SRI NASU-NSAU broker which generates and executes workflow, and delivers the flood maps to
the CEODE portal. The same applies to the broker operated at the CEODE side that provides flood mapping services
using optical satellite data. In order to get access to the portal the user should have a valid certificate. The SRI NASU-
NSAU runs the VOMS server to manage with this issue.
Workflow for flood mapping from satellite SAR imagery. Within the infrastructure an automated workflow of
satellite synthetic-aperture radar (SAR) data acquisition, processing and visualization, and corresponding geospatial
services for flood mapping from satellite SAR imagery were developed. The data are automatically downloaded from
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the ESA rolling archives where satellite images are available within 2-4 hours after their acquisition. Both programming
and graphical inter-faces were developed to enable search, discovery and acquisition of data. Through the portal a user
can perform a search for the SAR image file based on geographical region and a time range. A list of available SAR
imagery is returned and the user can select a file in order to generate a flood map. The file is transferred to the resources
of the SRI NASU-NSAU and a workflow is automatically generated and executed on the resources of the infrastructure
(Fig. 3).
Fig. 2. Architecture of service-oriented Ukrainian-Chinese system for disaster management
User VOMS
validateCert()
Portal
enters
Server
Navigate, select
SearchData()
Catalog Sevice
searchMetadata()
List of data
createDataLayersvizualizeDataShapes
selectData generateWorkflow
CPU
executeWorkflow
links to KML, WMS, shape filesvisualization of data
Fig. 3. UML sequence diagram of workflow for flood mapping from satellite SAR imagery
A neural network approach to SAR image segmentation and classification was developed [5, 9, 10]. The
workflow of data processing is as follows:
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Захист інформації
1. Geometric (orthorectification) and radiometric correction of the SAR image using digital elevation model
(DEM). Regions with shadows and layover are identified at this stage as well.
2. Processing of SAR image using a neural network classifier. The neural network assigns to each pixel of the
output image a binary value corresponding to one of two classes (‘‘Water’’ and ‘‘No water’’) taking as input a moving
window of image pixel intensities.
3. Removal of those regions that were detected as shadow/layover at the stage No. 1, since the pixel values in
these regions are not related to physical conditions of the environment.
4. Geocoding, i.e. transformation to geographical projection using ground-control points (GCPs) incorporated in
the SAR image;
Visualization of the results using ESRI shape files, KML (Keyhole Markup Language) files, and OGC WMS. To
benefit from the use of the Grid a parallel version of the method for flood mapping from satellite SAR imagery has been
developed. The parallelization of the image processing was implemented in the following way: a SAR image is split
into the uniform parts that are processed on different nodes using the OpenMP Application Program Interface
(www.openmp.org). The use of such infrastructure allowed us to considerably reduce the time required for image
processing and service delivery. In particular, it took approximately more than 1.5 h (depending on image size) to
execute the whole workflow on a single workstation. The use of Grid computing resources allowed us to reduce the
computational time to less than 20 minutes.
3. Enabling trust in service-oriented system for satellite data processing
Enabling security trust between different parties within a heterogeneous structural-complex system is one of the
main prerequisite for such integration. We investigated and applied two approaches: Public Key Infrastructure (PKI)
and reputation-based trust management. First, we review a general approach to integrating reputation management
system into the service-oriented structurally complex systems.
Integrating reputation into service-oriented structurally complex systems. Figure 4 shows a general framework
for integrating reputation management system into the service-oriented structurally complex systems.
Fig. 4. Integrating reputation into service-oriented structurally complex systems
The main components and system of this architecture are as follows:
— Users. They utilize services. The users can be human beings who get access to services through portals but
also can be third-party systems that utilize APIs. Users also form and participate in virtual organizations.
— Resource. Resources deliver services, form and participate in virtual organisations.
— Virtual organisations (VO). VO is a temporary or permanent coalition of geographically distributed
individuals, groups, organizations that share resources, capabilities (databases, computing resources etc.) and
information to reach common goals.
— Virtual Organisation Membership Service (VOMS). VOMS is a system for managing authorization data
within multi-institutional collaborations.
— Serice. Service is a functionality provided by resources and system components.
— Catalogue service provides a list of services that resources provide.
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Захист інформації
— Resource management system processes request from users, performs service searching based on user
requirements, and select appropriate resource to deliver a service. For selecting resources reputation acquired from
reputation management system is utilized.
— Reputation management system assess, stores and updates resource reputation. This system interacts with
monitoring system to acquire data on quality of service (QoS) metrics to calculate resources reputation. It also interacts
with resource management system to deliver resource reputation.
— Monitoring system is used to acquire data on user and service interactions.
Certificates. X.509 is the most widely used format which is supported by most of the existing software. To get
access to resources of the Grid system, a user should make a request to a Certificate Authority (CA) which is always a
known third party. The CA validates the information about the user and then signs the user certificate by the CA’s
private key. The certificate can thus be used to authenticate the user to grant access to the system. To provide a single
sign on and delegation capabilities the user can use the certificate and his private key to create a proxy certificate. This
certificate is signed not by CA but rather the user himself. The proxy certificate contains information about the user’s
identity and a special time stamp after which the certificate will no longer be accepted.
In order to enable Grid system integration with different middleware installed and security mechanisms and
policies used the following solutions were tested:
1. To create our own CAs and to enable the trust between them.
2. To obtain certificates from a well known CA, for example the European Policy Management Authority for
Grid Authentication (EUGridPMA, http://www.eugridpma.org).
3. To use a combined approach in which some of the Grid nodes accept only certificates from the local CA and
others accept certificates from a well known third party CAs.
Within the integration of the UASpaceGrid and the CEODE-CAS Grid the second and the third approaches were
verified. In such a case, the UASpaceGrid accepted the certificates issued by the local CA that was established using the
TinyCA, and certificates issued by the UGRID CA (https://ca.ugrid.org).
It is worth mentioning that Globus Toolkit v.4 and gLite v.3 middleware implement the same standard for the
certificates, but different standards for describing the certificate policies. That is why it is necessary to use two different
standards for describing the CA’s identity in a policy description file.
Reputation model. In order to enable trust between different parties, we also focused on estimating resources
reputation based on QoS metrics and SLA. In particular, we extended the existing utility-based reputation model for
virtual organizations in Grids [11] to address threats scenarios in the area of trust and reputation management [12]–[16].
These modifications included: assigning initial reputation to a new entity in VO, capturing alliance between consumer
and resource, defining a time decay function, and score function. The proposed reputation model was analyzed against
the existing security threat scenarios and how the model overcomes them. In particular, the model was effective in
countering such threats as individual malicious peers, malicious collectives, driving down the reputation of a reliable
peer, while there were some limitations in countering malicious collectives with camouflage.
The proposed model was evaluated not only in terms of countering security threats but also resource
management in service-oriented systems. A problem of on-line job scheduling was considered, and a general procedure
for integrating reputation into the scheduler using a non-linear trade-off scheme was presented. The advantage of using
such a scheme is that it provides a Pareto-optimal solution partially satisfying criteria with corresponding weights. The
experiments showed that the scheduler with knowledge of reputation using a non-linear trade-off scheme outperformed
a scheduler without knowledge of reputation on average 45% for all performance metrics used in the study. A non-
linear linear trade-off scheme for incorporating reputation into the scheduler of resource management system was
compared to a multiplication scheme, and showed better performance with a factor of 2. Currently, we are evaluating
the use of such a model for the GEOSS infrastructure.
4. Earth observation applications
Flood monitoring and risk assessment for Ukraine. The severe 2010 winter in Ukraine that was characterized by
large amount of snow and ice on rivers posed a high threat of floods during the melting period. In order to provide flood
risk assessment both synthetic-aperture radar (SAR) and optical satellite imagery were used. In particular, we acquired
more than 30 Envisat/ASAR scenes during the 2010 winter-spring period, and more than 50 archived scenes during
autumn 2009 to monitor the snow cover. These images were processed using resources of the service-oriented
infrastructure.
Satellite optical images were acquired using the EO-1 Sensor Web System. In particular, the OGC Sensor
Planning Service (SPS) was used for tasking the EO-1 satellite [17]–[19]. Through the UN-SPIDER RSO in Ukraine a
request was made from local authorities to acquire satellite images over the Kyiv city area due to a high risk of a flood
in spring 2010. The use of the Sensor Web and the Grid ensured a timely delivery of products to end-users. A sequence
of events starting from the notification of satellite tasking and ending with generation of final products is as follows:
Mon Apr 12, 2010 @ 10:33 pm — Notification on EO-1 tasking through Sensor Planning Service (SPS)
Tue Apr 13, 2010 @ 10:33 am — Image taken
Tue Apr 13, 2010 @ 04:30 pm — Image available at the NASA ftp server and automatically transferred
to the Grid system resources
Tue Apr 13, 2010 @ 11:30 pm — Generation of geospatial products using Grid computing resources
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Захист інформації
It took less than 12 h after image acquisition to generate geospatial products that were delivered to the Ukrainian
Ministry of Emergency Situations, the Council of National Security and Defence, and the Ukrainian
Hydrometeorological Centre. The information on river extent that was derived from the EO-1 image was used to
calibrate and validate hydrological models to produce various scenarios of water extent for flood risk assessment.
Flood mapping for China. In July 2007, following the flooding on the river Huaihe (China), SRI NASU-NSAU
and CEODE-CAS processed Envisat/ASAR and Radarsat-1 images that were acquired 15 and 19 July 2007,
respectively. The images were processed using computational resources of the service-oriented infrastructure. The
resulting flood maps were delivered using OGC WMS services.
Conclusions
In this paper we presented results that were achieved within the Wide Area Grid (WAG) project. In order to
benefit from using data from multiple sources, integration of the Ukrainian and Chinese flood mapping services that use
radar and optical satellite data was carried out. The paper discussed the use of certificate-based and reputation-based
trust enabling issues. The particular advantages of such integration are as follows: the system will benefit from the use
of services that are provided by different partners within the system; data resources are distributed within the system at
different sites (Ukrainian and Chinese), so this allows us to improve the quality of services delivered within the system;
the use of Grids enables the use of high-performance computing resources that in turn will decrease the time required
for satellite data processing and delivery of service which is critical for disaster management domain; the use of unified
Grid and OGC services provides scalability of the system while adding new components and services; such
collaboration provides valuable contribution and impact to the on-going large-scale international activities such as
creation of the GEOSS infrastructure.
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