%J SCIENTIFIC DATA
%O 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: MestersĂŠges Intelligencia Nemzeti LaboratĂłrium, MILAB, 138903            
            Funding details: European Commission, EC, RRF-2.3.1-21-2022-00004            
            Funding details: Nemzeti KutatĂĄsi, FejlesztĂŠsi ĂŠs InnovaciĂłs 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.
%V 10
%T CrySyS dataset of CAN traffic logs containing fabrication and masquerade attacks
%L publicatio29945
%A  Gazdag AndrĂĄs GĂĄbor
%A  Ferenc Rudolf
%A  ButtyĂĄn Levente
%I szte
%R MTMT:34448268 10.1038/s41597-023-02716-9
%D 2023
%N 1
%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. Š 2023, The Author(s).