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Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 1055-1065
Author(s):  
Mohammad R. Hassan ◽  
Feras A. Alnaimait ◽  
Qasem Kharma ◽  
Ashraf Sharah ◽  
Khalil H. Al-Shqeerat

In any multi–device / party systems supporting GRID and cloud-based applications, an essential constraint of these systems is the need of all tools and participants to interconnect with each other as members of a group in a secure approach. Group key management method is an essential functional element for any protected distributed communication setting. Key distribution method is a crucial factor in securing communication in grid computing. After the secure key management is executed, messages will be able to be securely exchanged between the grid units. A number of protocols have been proposed to maintain secure group key management. In this paper we present a new password base protocol for secure group key management in Grid computing environment, which is organized in two dynamic servicing layers: the grid application that needs grid services, and the grid services that act on behalf of the user.


2021 ◽  
Author(s):  
◽  
David Stirling

<p>Client honeypots are devices for detecting malicious servers on a network. They interact with potentially malicious servers and analyse the Web pages returned to assess whether these pages contain an attack. This type of attack is termed a 'drive-by-download'. Low-interaction client honeypots operate a signature-based approach to detecting known malicious code. High- interaction client honeypots run client applications in full operating systems that are usually hosted by a virtual machine. The operating systems are either internally or externally monitored for anomalous behaviour. In recent years there have been a growing number of client honeypot systems being developed, but there is little interoperability between systems because each has its own custom operational scripts and data formats. By creating interoperability through standard interfaces we could more easily share usage of client honeypots and the data collected. Another problem is providing a simple means of managing an installation of client honeypots. Work ows are a popular technology for allowing end-users to co-ordinate e-science experiments, so these work ow systems can potentially be utilised for client honeypot management. To formulate requirements for management we ran moderate-scale scans of the .nz domain over several months using a manual script-based approach. The main requirements were a system that is user-oriented, loosely-coupled, and integrated with Grid computing|allowing for resource sharing across organisations. Our system design uses Grid services (extensions to Web services) to wrap client honeypots, a manager component acts as a broker for user access, and workflows orchestrate the Grid services. Our prototype wraps our case study - Capture-HPC -with these services, using the Taverna workflow system, and a Web portal for user access. When evaluating our experiences we found that while our system design met our requirements, currently a Java-based application operating on our Web services provides some advantages over our Taverna approach - particularly for modifying workflows, maintainability, and dealing with  failure. The Taverna workflows, however, are better suited for the data analysis phase and have some usability advantages. Workflow languages such as Taverna are still relatively immature, so improvements are likely to be made. Both of these approaches are significantly easier to manage and deploy than the previous manual script-based method.</p>


2021 ◽  
Author(s):  
◽  
David Stirling

<p>Client honeypots are devices for detecting malicious servers on a network. They interact with potentially malicious servers and analyse the Web pages returned to assess whether these pages contain an attack. This type of attack is termed a 'drive-by-download'. Low-interaction client honeypots operate a signature-based approach to detecting known malicious code. High- interaction client honeypots run client applications in full operating systems that are usually hosted by a virtual machine. The operating systems are either internally or externally monitored for anomalous behaviour. In recent years there have been a growing number of client honeypot systems being developed, but there is little interoperability between systems because each has its own custom operational scripts and data formats. By creating interoperability through standard interfaces we could more easily share usage of client honeypots and the data collected. Another problem is providing a simple means of managing an installation of client honeypots. Work ows are a popular technology for allowing end-users to co-ordinate e-science experiments, so these work ow systems can potentially be utilised for client honeypot management. To formulate requirements for management we ran moderate-scale scans of the .nz domain over several months using a manual script-based approach. The main requirements were a system that is user-oriented, loosely-coupled, and integrated with Grid computing|allowing for resource sharing across organisations. Our system design uses Grid services (extensions to Web services) to wrap client honeypots, a manager component acts as a broker for user access, and workflows orchestrate the Grid services. Our prototype wraps our case study - Capture-HPC -with these services, using the Taverna workflow system, and a Web portal for user access. When evaluating our experiences we found that while our system design met our requirements, currently a Java-based application operating on our Web services provides some advantages over our Taverna approach - particularly for modifying workflows, maintainability, and dealing with  failure. The Taverna workflows, however, are better suited for the data analysis phase and have some usability advantages. Workflow languages such as Taverna are still relatively immature, so improvements are likely to be made. Both of these approaches are significantly easier to manage and deploy than the previous manual script-based method.</p>


Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7176
Author(s):  
Rob Shipman ◽  
Rebecca Roberts ◽  
Julie Waldron ◽  
Chris Rimmer ◽  
Lucelia Rodrigues ◽  
...  

Vehicle-to-grid services make use of the aggregated capacity available from a fleet of vehicles to participate in energy markets, help integrate renewable energy in the grid and balance energy use. In this paper, the critical components of such a service are described in the context of a commercial service that is currently under development. Key among these components is the prediction of available capacity at a future time. In this paper, we extend a previous work that used a deep learning recurrent neural network for this task to include online machine learning, which enables the network to continually refine its predictions based on observed behaviour. The coronavirus pandemic that was declared in 2020 resulted in closures of the university and substantial changes to the behaviour of the university fleet. In this work, the impact of this change in vehicles usage was used to test the predictions of a network initially trained using vehicle trip data from 2019 with and without online machine learning. It is shown that prediction error is significantly reduced using online machine learning, and it is concluded that a similar capability will be of critical importance for a commercial service such as the one described in this paper.


2021 ◽  
pp. 118133
Author(s):  
Yangyang Fu ◽  
Zheng O'Neill ◽  
Jin Wen ◽  
Amanda Pertzborn ◽  
Steven T. Bushby

2021 ◽  
Author(s):  
Anand Narayan ◽  
Marcel Klaes ◽  
Sebastian Lehnhoff ◽  
Christian Rehtanz
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