This dataset is used to illustrate an application of the klm-based profiling and preventing security attack (klm-PPSA) system. The klm-PPSA system is developed to detect, profile, and prevent (un)known security attacks before accessing the cloud services/resources. This dataset was generated based on one-user logical scenarios when attempting to access cloud services/resources. You will find attached the comma-separated values file of the dataset, which contains 460 instances and 13 attributes of the dependent and independent variables.

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Deployment of serverless functions depends on various factors. This dataset presents deployment time of a Python serverless function with various deployment package size, deployed on 6 regions of AWS and 6 regions of IBM. Deployment scripts are executed from Innsbruck, Austria.

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Effects of the "spawn start" of the Monte Carlo serverless function that simulates Pi.

The functions are orchestrated as a workflow and executed with the xAFCL enactment engine (https://doi.org/10.1109/TSC.2021.3128137) on three regions (US, EU, Asia) of three cloud providers AWS Lambda, Google Cloud Functions, and IBM Cloud Functions.

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This data set was generated and used in determining the workability of a homemade Intelligent IoT Weather Station Using an Embedded System.

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This file provides the main descriptive information about this services dependency graphs dataset to model web services compositions.

This dataset represents the services dependency graphs (SDGs) generated by our developed Mutual Information-based Services Dependency (MISD) model for 4 public services datasets.

 

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This dataset is collected from a running Decentralized Application (DApp). When the DApp receives transaction requests stably, we add system pressures with stress-ng, such as I/O pressure to inject anomalies manually. We increase disk pressure for 20 minutes every hour. We keep monitoring the DApp for 12 hours and collect data every 15 seconds, resulting in 3237 samples and 229 resource-related metrics for our experiments. In addition, an important metric that represents the number of transaction failures can be seen as the anomaly indicator of the DApp.

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This dataset is an experimental result of the paper “Performance Evaluation for Geographically Distributed Blockchain-based Services in a Cloud Computing Environment”. The Geographically Distributed Cloud Performance Evaluation Ambassador (GDCPEA) is deployed on each Go Ethereum (Geth) node to measure the elapsed time from the start to the end of the Geth main operations.

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It is now widely known fact that the Cloud computing and Software defined network paradigms have received a wide acceptance from researchers, academia and the industry. But the wider acceptance of cloud computing and SDN paradigms are hampered by increasing security threats. One of the several facts is that the advancements in processing facilities currently available are implicitly helping the attackers to attack in various directions. For example, it is visible that the conventional DoS attacks are now extended to cloud environments as DDoS attacks.

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1038 Views

With the modern day technological advancements and the evolution of Industry 4.0, it is very important to make sure that the problem of Intrusion detection in Cloud , IoT and other modern networking environments is addressed as an immediate concern. It is a fact that Cloud and Cyber Physical Systems are the basis for Industry 4.0. Thus, intrusion detection in cyber physical systems plays a crucial role in Industry 4.0. Here, we provide the an intrusion detection dataset for performance evaluation of machine learning and deep learning based intrusion detection systems.

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857 Views

This dataset contains measurements of TPC-C benchmark executions in MySQL server deployed in Google Cloud Platform.

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