This is the dataset provided and collected while "Car Hacking: Attack & Defense Challenge" in 2020. We are the main organizer of the competition along with Culture Makers and Korea Internet & Security Agency. We are very proud of releasing these valuable datasets for all security researchers for free.
The competition aimed to develop attack and detection techniques of Controller Area Network (CAN), a widely used standard of in-vehicle network. The target vehicle of competition was Hyundai Avante CN7.
|Round||Type||Description||# Normal||# Attack||# Rows|
|Preliminary||Training||Normal and four types of attacks dataset with class||3,372,743||299,408||3,672,151|
|Submission||Normal and four types of attacks dataset with class|
(during the competition, without class)
|Final||Submission||Normal and five attacks (4 spoofings, 1 fuzzing) dataset with class|
(during the competition, without class)
Normal: Normal traffic in CAN bus.
Attack: Attack traffic injected. Four types of attacks are included -- Flooding, Spoofing, Replay, Fuzzing.
Flooding: Flooding attack aims to consume CAN bus bandwidth by sending a massive number of messages.
Spoofing: CAN messages are injected to control certain desired function.
Replay: Replay attack is to extract normal traffic at a specific time and replay (inject) it into the CAN bus.
Fuzzing: Random messages are injected to cause unexpected behavior of the vehicle.
This work was supported by Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00866, Challenges for next generation security R&D).
The MIMOSigRef-SD dataset was created with the goal to support the research community in the design and development of novel multiple-input multiple-ouotput (MIMO) transceiver architectures. It was recorded using software radios as transmitters and receivers, and a wireless channel emulator to facilitate a realistic representation of a variety of different channel environments and conditions.
The MIMOSigRef-SD dataset is provided in the form of 8 individual TAR files. Each file represents one of the 8 modulation schemes utilized in our dataset. Within each file, the data is organized in a similar format: Naming of the contained folders represents the modulation scheme and order, channel environment, MIMO configuration, and type of MIMO. An example of such naming is: 16QAM – Vehicular B – (TX2 – RX1) – Spatial Diversity. This provides easy access to the information of interest.
Protein molecules are inherently dynamic and modulate their interactions with different molecular partners by accessing different tertiary structures under physiological conditions.Elucidating such structures remains challenging. Current momentum in deep learning and the powerful performance of generative adversarial networks (GANs) in complex domains, such as computer vision, inspires us to investigate GANs on their ability to generate physically-realistic protein tertiary structures.
Generated data, input data and saved models for the publication
Taseef Rahman, Yuanqi Du, Liang Zhao, and Amarda Shehu. Generative Adversarial Learning of Protein Tertiary Structures. Molecules, 2021.
is made available. Instructions accompany the data in a ReadMe.txt in each folder respectively for the ease of use.
The dataset is generated by performing different MiTM attacks in the synthetic electric grid in RESLab testbed at Texas A&M University, US. The testbed primarily consists of a dynamic power system simulator (Powerworld Dynamic Studio), network emulator (CORE), Snort IDS, open DNP3 master and Elasticsearch's Packetbeat index. There are raw and processed files that can be used by security enthusiasts to develop new features and also to train IDS using our feature space respectively.
This dataset is for short-term spatio-temporal PV forecasting.
This dataset consists of three two parts. The first part is the spatio-temporal PV dataset which obatined from different PV sites. The second part is the corresponding weather datasets, including temperature, wind speed, wind direction, etc.
The dataset also contains the demo codes for showing the concept of a machine learning based PV forecasting model.
More information will be added in the future.
The dataset is part of the MIMIC database and specifically utilise the data corresponding to two patients with ids 221 and 230.
This data set is the result of model test trained on the basis of the Stanford earthquake dataset (stead): a global data set of seismic signals for AI, which can effectively get the seismic signal and the arrival time of seismic phase from the image, so as to prove the effectiveness of this model
Online Machine Learning for Energy-Aware Multicore Real-Time Embedded Systems Dataset is a Dataset composed of Hardware Performance Counters extracted from a Multicore Real-Time Embedded System. This Dataset encompasses every Monitorable Performance counters in a Cortex-A53 quad-core processor, totaling 54 performance counters, which are sampled periodically through a non-Intrusive Monitoring Framework implemented over Embedded Parallel Operating System (EPOS), a Real-Time Operating System.