The dataset is generated by performing different Man-in-the-Middle (MiTM) attacks in the synthetic cyber-physical electric grid in RESLab Testbed at Texas AM University, US. The testbed consists of a real-time power system simulator (Powerworld Dynamic Studio), network emulator (CORE), Snort IDS, open DNP3 master, SEL real-time automation controller (RTAC), and Cisco Layer-3 switch. With different scenarios of MiTM attack, we implement a logic-based defense mechanism in RTAC and save the traffic data and related cyber alert data under the attack.

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Port scanning attack is popular method to map a remote network or identify operating systems and applications. It allows the attackers to discover and exploit the vulnerabilities in the network.

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This dataset supports researchers in the validation process of solutions such as Intrusion Detection Systems (IDS) based on artificial intelligence and machine learning techniques for the detection and categorization of threats in Cyber Physical Systems (CPS). To that aim, data have been acquired from a water distribution hardware-in-the-loop testbed which emulates water passage between nine tanks via solenoid-valves, pumps, pressure and flow sensors. The testbed is composed by a real partition which is virtually connected to a simulated one.

Instructions: 

This dataset has related to the paper "A hardware-in-the-loop Water Distribution Testbed (WDT) dataset for cyber-physical security testing".
We provide four different acquisitions:
1) A normal acquisition without attacks ("normal.csv" for network traffic and "dataset_norm.csv" for physical measures)
2) Three acquisitions where different types of attacks and physical faults are reproduced ("attack_1.csv", "attack_2.csv" and "attack_3.csv" for network traffic and "dataset_att_1.csv", "dataset_att_2.csv" and "dataset_att_3.csv" for physical measures)
In addition to .csv files we provide four .pcap files ("attack_1.pcap", "attack_2.pcap", "attack_3.pcap" and "normal.pcap") which refer to network acquisitions for the four previous scenarios.
A README.xlsx file summarizes the key features of the entire dataset.

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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.

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This data set is from a recent biological molecular communication (MC) testbed and provides a set of experimental measurement data.In particular, the MC testbed is realized using Escherichia coli (E. coli) bacteria that express the light-driven proton pump gloeorhodopsin (GR) from Gloeobacter violaceus (G.

Instructions: 

Package:

- Comprehensive description of the data set and experimental procedure: supplement.pdf

- Publication: A Molecular Communication Testbed Based on Proton Pumping Bacteria: Methods and Data, IEEE Transactions on Molecular, Biological, and Multi-Scale Communications

- Signals for 18 experiments of types "Dark Adaption", "Control Response", "Long On-Off Response", and "Bit Response": SignalData.zip

- Signals for 12 noise measurements: NoiseData.zip

- Matlab script for visualization: example_DisplayData.m

 

Citing:

- Reference to the experimental system: Biological Optical-to-Chemical Signal Conversion Interface: A Small-Scale Modulator for Molecular Communications, https://ieeexplore.ieee.org/document/8467351

- Reference to this data set: A Molecular Communication Testbed Based on Proton Pumping Bacteria: Methods and Data, IEEE Transactions on Molecular, Biological, and Multi-Scale Communications

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