This dataset contains segmented taxi trajectories with passengers within Bangkok, which are extracted from taxi GPS data during the Songkran Festival period (April 11–17, 2019), published by the ITIC Foundation (https://org.iticfoundation.org/).
Safety assessment of Cyber-Physical Systems (CPS) requires a tremendous amount of effort as the complexity of cyber-physical systems is increasing. A well-known approach for the safety assessment of CPS is Fault Injection (FI). The goal of fault injection is to find a catastrophic fault that can fail the system by injecting faults into it. These catastrophic faults are less likely to happen, and finding it requires tremendous labor and cost.
Hydrothermal unit commitment dataInput data, optimization models and output
Two dataset collected by USkin tactile sensors for detecting grasping stability and slip detection during lifting objects.
This dataset will contains some simulation models in Matlab/Simulink of four most used active anti-islanding methods namely Active Frequency Drift (AFD), Sandia Frequency Shift (SFS), Slip Mode frequency Shift (SMS), Sandia Voltage Shift (SVS).
The dataset represents the input data on which the article Bayesian CNN-BiLSTM and Vine-GMCM Based Probabilistic Forecasting of Hour-Ahead Wind Farm Power Outputs, is based. The data consist of a two-year hourly time series of measured wind speed and direction, air density, and production of two wind farms (WTs) in Croatia (Bruška and Jelinak). In addition to the two listed WTs, measurements of two nearby WTs (Glunca and Zelengrad) are also attached in training files (these WPPs are not directly analyzed in the article).
The dataset originally was taken from DAIAD, which has the mechanism to monitor the water consumption in real time using a validated platform for different cities. These datasets had the record of different water consumption values taken from the smart water meters that indicates, total water consumption by different users in Litres with the time interval of one hour for a year.
The dataset includes the sweep scanning paths and measured points in two experiments.
Cloud forensics is different than digital forensics because of the architectural implementation of the cloud. In an Infrastructure as a Service (IaaS) cloud model. Virtual Machines (VM) deployed over the cloud can be used by adversaries to carry out a cyber-attack using the cloud as an environment.
About the dataset
The dataset generated is a KVM monitoring dataset however we proposed a novel feature-set. The methodology used to generate these novel features is explained in https://www.degruyter.com/document/doi/10.1515/comp-2022-0241/html. where the features can be used to train ML models for evidence detection.
The second portion of the dataset is published under the standard dataset of IEEE Dataport under the name of Memory Dumps of Virtual Machines for Cloud Forensics.
How to use
These two datasets can be used together as they are the outcome of the same experiment. Memory dumps have timestamp and VMID, UUID features.
This Dataset can be used to study the impact of an attack (origin) on the Rate of Resource utilization of a VM monitored at the hypervisor.
The ID of the VM
unique identifier of the domain
Rate of received bytes from the network
Rate of received packets from the network
Rate of the number of receive errors from the network
Rate of the number of received packets dropped from the network
Rate of transmitted bytes from the network
Rate of transmitted packets from the network
Rate of the number of transmission errors from the network
Rate of the number of transmitted packets dropped from the network
Rate of time spent by vCPU threads executing guest code
Rate of time spent in kernel space
Rate of time spent in userspace
Rate of running state
Rate of maximum memory in kilobytes
Rate of memory used in kilobytes
Rate of the number of virtual CPUs chaged
Rate of CPU time used in nanoseconds
Rate of Current balloon value (in KiB)
Rate of The amount of data read from swap space (in KiB)
Rate of The amount of memory written out to swap space (in KiB)
Rate of The number of page faults where disk IO was required
Rate of The number of other page faults
Rate of The amount of memory left unused by the system (in KiB)
Rate of The amount of usable memory as seen by the domain (in KiB)
Rate of The amount of memory that can be reclaimed by balloon without causing host swapping (in KiB)
Rate of The timestamp of the last update of statistics (in seconds)
Rate of The amount of memory that can be reclaimed without additional I/O, typically disk caches (in KiB)
Rate of The number of successful huge page allocations initiated from within the domain
Rate of The number of failed huge page allocations initiated from within the domain
Rate of Resident Set Size of the running domain's process (in KiB)
Rate of the number of reading requests on the vda block device
Rate of the number of reading bytes on the vda block device
Rate of the number of write requests on the vda block device
Rate of the number of write requests on vda the block device
Rate of the number of errors in the vda block device
Rate of the number of read requests on the hda block device
Rate of the number of read bytes on the had block device
Rate of the number of write requests on the hda block device
Rate of the number of write bytes on the hda block device
Rate of the number of errors in the hda block device