%0 Journal Article
%@ 2052-4463
%A  Gazdag András Gábor
%A  Ferenc Rudolf
%A  Buttyán Levente
%A Szoftverfejlesztés Tanszék SZTE / TTIK / INF SZFT [2016-],
%A Hálózati Rendszerek és Szolgáltatások Tanszék BME / VIK HRSZT [2013-],
%D 2023
%F publicatio:29945
%J SCIENTIFIC DATA
%N 1
%T CrySyS dataset of CAN traffic logs containing fabrication and masquerade attacks
%U http://publicatio.bibl.u-szeged.hu/29945/
%V 10
%X Despite their known security shortcomings, Controller Area Networks are widely used in modern vehicles. Research in the field has already proposed several solutions to increase the security of CAN networks, such as using anomaly detection methods to identify attacks. Modern anomaly detection procedures typically use machine learning solutions that require a large amount of data to be trained. This paper presents a novel CAN dataset specifically collected and generated to support the development of machine learning based anomaly detection systems. Our dataset contains 26 recordings of benign network traffic, amounting to more than 2.5 hours of traffic. We performed two types of attack on the benign data to create an attacked dataset representing most of the attacks previously proposed in the academic literature. As a novelty, we performed all attacks in two versions, modifying either one or two signals simultaneously. Along with the raw data, we also publish the source code used to generate the attacks to allow easy customization and extension of the dataset. © 2023, The Author(s).
%Z CrySyS Lab, Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary                         Department of Software Engineering, University of Szeged, Szeged, Hungary                         Export Date: 22 December 2023                         Correspondence Address: Gazdag, A.; CrySyS Lab, Hungary; email: andras.gazdag@crysys.hu                         Funding details: Mesterséges Intelligencia Nemzeti Laboratórium, MILAB, 138903                         Funding details: European Commission, EC, RRF-2.3.1-21-2022-00004                         Funding details: Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA                         Funding text 1: This work has been supported by the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory and Project no. 138903 implemented with the support provided by the Ministry of Innovation and Technology from the National Research, Development, and Innovation Fund, financed under the FK_21 funding scheme.