Security
SCVIC-CIDS-2021 is a novel dataset that combines network- and host-based data.
SCVIC-CIDS-2021 is derived from the meta-data (i.e., network packets, system logs and labeling information) from the well-known benchmark dataset, CIC-IDS-2018 (Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization”, 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018 ) .
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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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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). The dataset contains dozens of ‘repeat sessions’ per each user, where ‘repeat sessions’ are sessions during which the user is asked to complete the same logical task in a guided online environment (e.g., play an online game involving the same sequence of steps and intermediate objectives).
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<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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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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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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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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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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