Security
# 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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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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1080 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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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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4120 Views
Nowadays, with the rapid increase in the number of applications and networks, the number of cyber multi-step attacks has been increasing exponentially. Thus, the need for a reliable and acceptable Intrusion Detection System (IDS) solution is becoming urgent to protect the networks and devices. However, implementing a robust IDS needs a reliable and up-to-date dataset in order to capture the behaviors of the new types of attacks, especially multi-step attacks. In this work, a new benchmark Multi-Step Cyber-Attack Dataset (MSCAD) is introduced.
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3393 Views
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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1885 Views
Testchip measured challenge-response pairs from a non-monotonically quantized strong PUF.
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To generate the experimental datasets, we collect popular container applications mentioned in B4 like email and database. We also collect some applications based on containers mentioned in other papers. We use these applications as templates and generate experimental datasets by massive replication. To make the dataset closer to reality, we also randomly attach user-specific labels to containers and policies.
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The picture shows the operation result of image security retrieval. The experiment was validated on five common data sets.
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Anonymous network traffic is more pervasive than ever due to the accessibility of services such as virtual private networks (VPN) and The Onion Router (Tor). To address the need to identify and classify this traffic, machine and deep learning solutions have become the standard. However, high-performing classifiers often scale poorly when applied to real-world traffic classification due to the heavily skewed nature of network traffic data.
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804 Views