TY - JOUR AU - Gazdag, András Gábor AU - Ferenc, Rudolf AU - Buttyán, Levente TI - CrySyS dataset of CAN traffic logs containing fabrication and masquerade attacks JF - SCIENTIFIC DATA J2 - SCI DATA VL - 10 PY - 2023 IS - 1 PG - 11 SN - 2052-4463 DO - 10.1038/s41597-023-02716-9 UR - https://m2.mtmt.hu/api/publication/34448268 ID - 34448268 N1 - 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. AB - 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). LA - English DB - MTMT ER - TY - CHAP AU - Sándor, József AU - Nagy, Roland AU - Buttyán, Levente TI - PATRIoTA: A Similarity-based IoT Malware Detection Method Robust Against Adversarial Samples T2 - 2023 IEEE International Conference on Edge Computing and Communications (EDGE) PB - IEEE SN - 9798350304831 PY - 2023 SP - 344 EP - 353 PG - 10 DO - 10.1109/EDGE60047.2023.00057 UR - https://m2.mtmt.hu/api/publication/34126367 ID - 34126367 N1 - Export Date: 19 October 2023 Correspondence Address: Sandor, J.; Budapest University of Technology and EconomicsHungary; email: jsandor@crysys.hu LA - English DB - MTMT ER - TY - CHAP AU - Fuchs, Gábor AU - Nagy, Roland AU - Buttyán, Levente TI - A Practical Attack on the TLSH Similarity Digest Scheme T2 - Proceedings of the 18th International Conference on Availability, Reliability and Security PB - Association for Computing Machinery (ACM) CY - New York, New York SN - 9798400707728 PY - 2023 SP - 1 EP - 10 PG - 10 DO - 10.1145/3600160.3600173 UR - https://m2.mtmt.hu/api/publication/34123297 ID - 34123297 N1 - Funding Agency and Grant Number: European Union [RRF-2.3.1-21-2022-00004]; National Research, Development and Innovation Fund of Hungary under the 2018-1.2.1-NKP funding scheme [2018-1.2.1-NKP-2018-00004] Funding text: The research presented in this paper was supported by the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory. The presented work also builds on results of the SETIT Project (2018-1.2.1-NKP-2018-00004), which was implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the 2018-1.2.1-NKP funding scheme. LA - English DB - MTMT ER - TY - CHAP AU - Sándor, J. AU - Nagy, Roland AU - Buttyán, Levente ED - Gunduz, Deniz ED - Malekzadeh, Mohammad ED - Önen, Melek ED - Sagduyu, Yalin ED - Yi, Shi ED - Junqing, Zhang TI - Increasing the Robustness of a Machine Learning-based IoT Malware Detection Method with Adversarial Training T2 - WiseML'23: Proceedings of the 2023 ACM Workshop on Wireless Security and Machine Learning PB - Association for Computing Machinery, Inc CY - New York, New York SN - 9798400701337 T3 - WiseML 2023 - Proceedings of the 2023 ACM Workshop on Wireless Security and Machine Learning PY - 2023 SP - 3 EP - 8 PG - 6 DO - 10.1145/3586209.3591401 UR - https://m2.mtmt.hu/api/publication/34104403 ID - 34104403 LA - English DB - MTMT ER - TY - CHAP AU - Buttyán, Levente AU - Nagy, Roland AU - Papp, Dorottya ED - Fazekas, István TI - SIMBIoTA++: Improved Similarity-based IoT Malware Detection T2 - 2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway (NJ) SN - 9781665496520 PY - 2022 SP - 51 EP - 56 PG - 6 DO - 10.1109/CITDS54976.2022.9914145 UR - https://m2.mtmt.hu/api/publication/33207086 ID - 33207086 N1 - Budapest University of Technology and Economics, ELKH-BME Information Systems Research Group, CrySyS Lab, Budapest, Hungary Budapest University of Technology and Economics, CrySyS Lab, Budapest, Hungary Export Date: 15 November 2022 LA - English DB - MTMT ER - TY - JOUR AU - Bocsok, Viktor AU - Buttyán, Levente TI - Automotive cybersecurity - Is the legal framework ready for the challenge? - part 2. Overview of the Hungarian legislation TS - Overview of the Hungarian legislation JF - INFOKOMMUNIKÁCIÓ ÉS JOG J2 - INFOKOMMUNIKÁCIÓ JOG VL - 19 PY - 2022 IS - 78 SP - 8 EP - 19 PG - 12 SN - 1786-0776 UR - https://m2.mtmt.hu/api/publication/33187090 ID - 33187090 LA - English DB - MTMT ER - TY - CHAP AU - Nagy, Roland AU - Bak, M. AU - Papp, Dorottya AU - Buttyán, Levente ED - Vilmos, Andras ED - Marton, Anna ED - Kehagias, Dionysios ED - Jankovic, Marija ED - Gelenbe, Erol TI - T-RAID: TEE-based Remote Attestation for IoT Devices T2 - Security in Computer and Information Sciences VL - 1596 CCIS PB - Springer Netherlands CY - Cham SN - 9783031093562 T3 - Communications in Computer and Information Science, ISSN 1865-0929 ; 1596. PY - 2022 SP - 76 EP - 88 PG - 13 DO - 10.1007/978-3-031-09357-9_7 UR - https://m2.mtmt.hu/api/publication/33031593 ID - 33031593 N1 - Export Date: 28 July 2022 Correspondence Address: Buttyán, L.; Laboratory of Cryptography and System Security (CrySyS Lab), Hungary; email: buttyan@crysys.hu AB - The Internet of Things (IoT) consists of network-connected embedded devices that enable a multitude of new applications, but also create new risks. In particular, embedded IoT devices can be infected by malware. Operators of IoT systems not only need malware detection tools, but also scalable methods to reliably and remotely verify malware freedom of their IoT devices. In this paper, we address this problem by proposing T-RAID, a remote attestation scheme for IoT devices that takes advantage of the security guarantees provided by a Trusted Execution Environment running on each device. LA - English DB - MTMT ER - TY - CHAP AU - Gazdag, András Gábor AU - Lupták, György AU - Buttyán, Levente ED - Vilmos, Andras ED - Marton, Anna ED - Kehagias, Dionysios ED - Jankovic, Marija ED - Gelenbe, Erol TI - Correlation-Based Anomaly Detection for the CAN Bus T2 - Security in Computer and Information Sciences PB - Springer Netherlands CY - Cham SN - 9783031093562 T3 - Communications in Computer and Information Science, ISSN 1865-0929 ; 1596. PY - 2022 SP - 38 EP - 50 PG - 13 DO - 10.1007/978-3-031-09357-9_4 UR - https://m2.mtmt.hu/api/publication/32918688 ID - 32918688 N1 - Laboratory of Cryptography and System Security (CrySyS Lab), Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary Ukatemi Technologies, Budapest, Hungary Export Date: 28 July 2022 Correspondence Address: Gazdag, A.; Laboratory of Cryptography and System Security (CrySyS Lab), Hungary; email: agazdag@crysys.hu LA - English DB - MTMT ER - TY - CHAP AU - Papp, Dorottya AU - Ács, Gergely AU - Nagy, Roland AU - Buttyán, Levente ED - Bastieri, D ED - Wills, G ED - Kacsuk, Péter ED - Chang, V TI - SIMBIoTA-ML: Light-weight, Machine Learning-based Malware Detection for Embedded IoT Devices T2 - Proceedings of the 7th International Conference on Internet of Things, Big Data and Security, IoTBDS 2022 PB - SciTePress CY - Setubal SN - 9789897585647 T3 - IoTBDS, ISSN 2184-4976 PY - 2022 SP - 55 EP - 66 PG - 12 DO - 10.5220/0011080200003194 UR - https://m2.mtmt.hu/api/publication/32820993 ID - 32820993 N1 - Funding Agency and Grant Number: National Research, Development and Innovation Fund of Hungary [2018-1.2.1-NKP-2018-00004]; Ministry of Innovation and Technology NRDI Office [2018-1.2.1-NKP] Funding text: The presented work was carried out within the SETIT Project (2018-1.2.1-NKP-2018-00004), which has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the 2018-1.2.1-NKP funding scheme. The research was also supported by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program. The authors would like to thank Zolt ' an Iuhos for his help in implementing the experiments. AB - Embedded devices are increasingly connected to the Internet to provide new and innovative applications in many domains. However, these devices can also contain security vulnerabilities, which allow attackers to compromise them using malware. In this paper, we present SIMBIoTA-ML, a light-weight antivirus solution that enables embedded IoT devices to take advantage of machine learning-based malware detection. We show that SIMBIoTA-ML can respect the resource constraints of embedded IoT devices, and it has a true positive malware detection rate of ca. 95%, while having a low false positive detection rate at the same time. In addition, the detection process of SIMBIoTA-ML has a near-constant running time, which allows IoT developers to better estimate the delay introduced by scanning a file for malware, a property that is advantageous in real-time applications, notably in the domain of cyber-physical systems. LA - English DB - MTMT ER - TY - JOUR AU - Buttyán, Levente AU - Ferenc, Rudolf TI - IoT Malware Detection with Machine Learning JF - ERCIM NEWS J2 - ERCIM NEWS PY - 2022 IS - 129 SP - 17 EP - 19 PG - 3 SN - 0926-4981 UR - https://m2.mtmt.hu/api/publication/32820855 ID - 32820855 AB - Embedded devices are increasingly connected to the Internet to provide new and innovative applications in many domains. However, these IoT devices can also contain security vulnerabilities, which allow attackers to compromise them using malware. We report on our recent work on using machine learning for efficient and effective malware detection on resource-constrained IoT devices. LA - English DB - MTMT ER -