This dataset contains multimodal sensor data collected from side-channels while printing several types of objects on an Ultimaker 3 3D printer. Our related research paper titled "Sabotage Attack Detection for Additive Manufacturing Systems" can be found here: https://doi.org/10.1109/ACCESS.2020.2971947. In our work, we demonstrate that this sensor data can be used with machine learning algorithms to detect sabotage attacks on the 3D printer.

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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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This dataset provides the data collected in the scope of a Systematic Literature Review (SLR) study that focuses on identifying and classifying the recent research practices pertaining to CPS development through MDE approaches. The dataset includes 140 research papers published during 2010-2018. Accordingly, a comprehensive analysis of various MDE approaches used in the development life-cycle of CPS is analyzed. The dataset can be useful for the researchers and practitioners.

Instructions: 

This repository contains the following:

1. Main files:
- “MDE4CPS Protocol sheet”: in this file, the protocol of the study to be followed is defined. It contains the research question, keywords, search strings, I&E criteria, Quality assurance and self-assessment, and Data extraction form.
- “Data extraction sheet”: this file contains the raw data that is extracted from the primary studies by answering the research question. The primary studies are illustrated in rows, while the questions of the study are posed in columns. This file also contains sheets for excluded papers, forward snowballing, categorization.
- Data extraction validation sheet

2. Data analysis:
This folder contains the analysis of the research questions where it contains a sperate file for each research question (i.e. from RQ1 to RQ6) in addition to a file for Quality assurance Analysis and another for the Bibliometrics & Demographics Analysis. Furthermore, the data analysis folder contains another folder “correlation Analysis” which contains the correlation analysis between the research questions.

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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 dataset contains requests execution times for comparison of direct requests and requests via API gateway to test API.

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