Security

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.

Paper: http://jtiik.ub.ac.id/index.php/jtiik/article/view/4107

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This dataset is in support of my research paper 'Comparative Non-Linear Flux Matrices & Thermal Losses in BLDC with Different Pole Pairs' .

Preprint : https://doi.org/10.36227/techrxiv.19687287.v1

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3363 Views

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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4036 Views

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Simulation Code

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Simulation Code

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51 Views

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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2324 Views

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.

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3559 Views

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