Security
This dataset is used for the identification of video in the internet traffic. The dataset was prepared by using Wireshark. It comprises of two types of traffic data, VPN (Virtual Private Network) or encrypted traffic data and Non-VPN or unencrypted traffic. The dataset consist of the data streams (.pcap) of 43 videos. Each video is played 50 times in both VPN and Non-VPN mode. The streams were obtained by setting-up a dummy client on a PC which plays a YouTube video and Wireshark is used to capture the internet traffic.
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1170 Views
The ReMouse dataset is collected in a guided environment, and it contains repeat sessions generated by the same human user(s). ReMouse dataset contains the mouse dynamics information of 100 users of mixed nationality, residing in diverse geographical regions and using different devices (hardware and software components).
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1382 Views
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1412 Views
Normal
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21
false
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false
EN-IN
X-NONE
X-NONE
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202 Views
<p>This dataset is the experimental dataset in "LogSummary: Unstructured Log Summarization in Online Services". We have abstracted and annotated part of the six open-source log analysis datasets(BGL, HDFS, HPC, Proxifier, ZooKeeper, Spark), and generate their summaries manually.</p>
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548 Views
This article presents the details of the Cardinal RF (CardRF) dataset. CardRF is acquired to foster research in RF- based UAV detection and identification or RF fingerprinting. RF signals were collected from UAV controllers, UAV, Bluetooth, and Wi-Fi devices. Signals are collected at both visual line-of-sight and beyond-line-of-sight. The assumptions and procedure for the data acquisition are presented. A detailed explanation of how the data can be utilized is discussed. CardRF is over 65 GB in storage memory.
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8164 Views
# Datasets
The datasets were collected from a software based simulation environment simulating a small scale IEC 61850 compliant substation with both the primary plant and the process bus.
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39 Views
Identifying patterns in the modus operandi of attackers is an essential requirement in the study of Advanced Persistent Threats. Previous studies have been hampered by the lack of accurate, relevant, and representative datasets of current threats. System logs and network traffic captured during attacks on real companies’ information systems are the best data sources to build such datasets. Unfortunately, for apparent reasons of companies’ reputation, privacy, and security, such data is seldom available.
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922 Views
Anomaly detection is a well-known topic in cybersecurity. Its application to the Internet of Things can lead to suitable protection techniques against problems such as denial of service attacks.
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3595 Views
The dataset has been developed in Smart Connected Vehicles Innovation Centre (SCVIC) of the University of Ottawa in Kanata North Technology Park.
In order to define a benchmark for Machine Learning (ML)-based Advanced Persistent Threat (APT) detection in the network traffic, we create a dataset named SCVIC-APT-2021, that can realistically represent the contemporary network architecture and APT characteristics. Please cite the following original article where this work was initially presented:
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3163 Views