Intrusion dataset
A key challenge in cybersecurity is the absence of a large-scale network dataset that accurately captures modern traffic patterns, diverse intrusion types, and comprehensive network activity. Existing benchmark datasets such as KDDCup99, NSL-KDD, GureKDD, and UNSW-NB15 require updates to reflect contemporary cyberattack signatures effectively.
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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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This dataset was created for the following paper: Seonghoon Jeong, Boosun Jeon, Boheung Chung, and Huy Kang Kim, "Convolutional neural network-based intrusion detection system for AVTP streams in automotive Ethernet-based networks," Vehicular Communications, DOI: 10.1016/j.vehcom.2021.100338.
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