The malicious traffic detection system monitors the communication between the industrial equipment and analyzes the protocol in real time. At the same time, we launch a variety of attacks on the industrial system, such as Denial of Service attack, Man-in-the-Middle attack and so on. These attacks are also the major threat in the ICS currently. Then, we collect and classify different kinds of attack flow. These flows are intercepted from multiple collection stations during different periods.

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The data that we used to test the performance of different encryption methods

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The Automation for Vehicles is the current trend, which transforms the merely conveyer to well-furnished moving home-like-feeling. As the country is opened to globalization, people’s income is rising. And the travelling is now become essential part of the life. So, governments prefer not only to raise their road’s quality but also the width of it. As a result, four track National Highway roads are flowing throughout the country like blood veins.

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privacy location data

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This dataset was generated on a small-scale process automation scenario using MODBUS/TCP equipment, for research on the application of ML techniques to cybersecurity in Industrial Control Systems. The testbed emulates a CPS process controlled by a SCADA system using the MODBUS/TCP protocol. It consists of a liquid pump simulated by an electric motor controlled by a variable frequency drive (allowing for multiple rotor speeds), which in its turn controlled by a Programmable Logic Controller (PLC).

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3226 Views

Device identification using network traffic analysis is being researched for IoT and non-IoT devices against cyber-attacks. The idea is to define a device specific unique fingerprint by analyzing the solely inter-arrival time (IAT) of packets as feature to identify a device. Deep learning is used on IAT signature for device fingerprinting of 58 non-IoT devices. We observed maximum recall and accuracy of 97.9% and 97.7% to identify device. A comparitive research GTID found using defined IAT signature that models of device identification are better than device type identification.

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All instructions are available at https://github.com/naneja/device-fingerprinting 

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494 Views

We have included the code of protocol verification for ProVerif (in .pv format) and Scyther (in .spdl format) in the “Supplementary Materials” (Supplementary-Materials.pdf) of the revised manuscript. Essentially, the .pdf file of the “Supplementary Materials” includes: A. The source code for the protocol verifier Scyther (in .spdl format); and B. The source code for the protocol verifier ProVerif (in .pv format).

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This dataset was collected from an industrial control system running the Modbus protocol. It is used to train a deep adversarial learning model. This model is used to generate fuzzing data in the same format as the real one. The data is a sequence of hexadecimal numbers. The followed generated data is produced by the already trained model.

 

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 This enclosed file comprises two parts: one is the social network simulation data set generated based on R-MART and power law distribution model; the other is the sourse code of alogorithm 1 to 6 for PBCN. 

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241 Views

Spectroreflectometric techniques for dactiloscopy and optical nail diagnostics are proposed and aproved on "DATACOLOR 3890"-based measuring system.

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134 Views

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