This dataset is from apache access log server. It contains: ip address, datetime, gmt, request, status, size, user agent, country, label. The dataset show malicious activity in IP address, request, and so on. You can analyze more as intrusion detection parameter.

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This dataset contains: ip address, datetime, gmt, request, status, size, user agent, country, label. Allowed traffic only from Indonesia, because the web is local purpose, so this dataset assume the traffic from abroad is prohobited.

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This dataset is composed of side channel information (e.g., temperatures, voltages, utilization rates) from computing systems executing benign and malicious code.  The intent of the dataset is to allow aritificial intelligence tools to be applied to malware detection using side channel information.

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'Test Dataset' folder:

-Real_sk:  random generated secret keys datasets

-Trace_data: traces generated from chosen ciphertexts

 

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There are two datasets: Drebin4000 and AMD6000.

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Cyber attacks are a growing concern for small businesses during COVID-19 . Be Protected While You Work. Upgrade Your Small Business's Virus Protection Today!

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Simulation Code

Instructions: 

To access the code, please click the Link  "Hidden Real Modulus RSA(HRM-RSA)"

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Simulation Code

Instructions: 

To see the code, please Click the URL "Hidden Modulus RSA (HRM-RSA)"?

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Network Address Translation (NAT), which is present in almost all routers and CPEs, maps private IP addresses to routable or public IP addresses. This feature has advantages such as reuse of private IP addresses but also has disadvantages such as creating “Shadow IT” where network admins do not have knowledge of all devices on their network. This dataset contains network traffic that is double-NATed thus replicating the scenario of shadow IT in an enterprise context.

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This data set contains the evaluation results of UMLsecRT:

  • All completed answers to the user-study on the usability of the adapted models.
  • The runtime-measurements and profiling outcomes of the experiment on the DaCapo benchmark.
Instructions: 

All results are formatted as CSV files (exported from LimeSurvey) or as LibreOffice spreadsheets (saved as *.odt)

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The dataset contains:
1. We conducted a A 24-hour recording of ADS-B signals at DAB on 1090 MHz with USRP B210 (8 MHz sample rate). In total, we got the signals from more than 130 aircraft.
2. An enhanced gr-adsb, in which each message's digital baseband (I/Q) signals and metadata (flight information) are recorded simultaneously. The output file path can be specified in the property panel of the ADS-B decoder submodule.
3. Our GnuRadio flow for signal reception.
4. Matlab code of the paper, wireless device identification using the zero-bias neural network.

Instructions: 

1. The "main.m" in Matlab code is the entry of simulation.
2. The "csv2mat" is a CPP program to convert raw records (adsb_records1.zip) of our gr-adsb into matlab manipulatable format. Matio library (https://github.com/tbeu/matio) is required.
3. The Gnuradio flowgraph is also provided with the enhanced version of gr-adsb, in which you are supposed to replace the original one (https://github.com/mhostetter/gr-adsb). And, you can specify an output file path in the property panel of the ADS-B decoder submodule.
4. Related publication: Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices, IEEE IoTJ (accepted for publication on 21 August 2020), DOI: 10.1109/JIOT.2020.3018677

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