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Zenden, I., Wang, H., Iacovazzi, A., Vahidi, A., Blom, R. & Raza, S. (2023). On the Resilience of Machine Learning-Based IDS for Automotive Networks. In: proc of IEEE Vehicular Networking Conference, VNC: . Paper presented at 14th IEEE Vehicular Networking Conference, VNC 2023.Instanbul. 26 April 2023 through 28 April 2023. (pp. 239-246). IEEE Computer Society
Öppna denna publikation i ny flik eller fönster >>On the Resilience of Machine Learning-Based IDS for Automotive Networks
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2023 (Engelska)Ingår i: proc of IEEE Vehicular Networking Conference, VNC, IEEE Computer Society , 2023, s. 239-246Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Modern automotive functions are controlled by a large number of small computers called electronic control units (ECUs). These functions span from safety-critical autonomous driving to comfort and infotainment. ECUs communicate with one another over multiple internal networks using different technologies. Some, such as Controller Area Network (CAN), are very simple and provide minimal or no security services. Machine learning techniques can be used to detect anomalous activities in such networks. However, it is necessary that these machine learning techniques are not prone to adversarial attacks. In this paper, we investigate adversarial sample vulnerabilities in four different machine learning-based intrusion detection systems for automotive networks. We show that adversarial samples negatively impact three of the four studied solutions. Furthermore, we analyze transferability of adversarial samples between different systems. We also investigate detection performance and the attack success rate after using adversarial samples in the training. After analyzing these results, we discuss whether current solutions are mature enough for a use in modern vehicles.

Ort, förlag, år, upplaga, sidor
IEEE Computer Society, 2023
Nyckelord
Adversarial AI/ML, Controller Area Network, Intrusion Detection System, Machine Learning, Vehicle Security, Computer crime, Control system synthesis, Controllers, Intrusion detection, Learning algorithms, Network security, Process control, Safety engineering, Automotive networks, Automotives, Autonomous driving, Controller-area network, Electronics control unit, Intrusion Detection Systems, Machine learning techniques, Machine-learning
Nationell ämneskategori
Reglerteknik
Identifikatorer
urn:nbn:se:ri:diva-65727 (URN)10.1109/VNC57357.2023.10136285 (DOI)2-s2.0-85163164299 (Scopus ID)9798350335491 (ISBN)
Konferens
14th IEEE Vehicular Networking Conference, VNC 2023.Instanbul. 26 April 2023 through 28 April 2023.
Anmärkning

This research is partially funded by the CyReV project(Sweden’s Innovation Agency, D-nr 2019-03071), partiallyby the H2020 ARCADIAN-IoT (Grant ID. 101020259), andH2020 VEDLIoT (Grant ID. 957197).

Tillgänglig från: 2023-08-11 Skapad: 2023-08-11 Senast uppdaterad: 2024-03-03Bibliografiskt granskad
Iacovazzi, A., Wang, H., Butun, I. & Raza, S. (2023). Towards Cyber Threat Intelligence for the IoT. In: Proceedings - 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023: . Paper presented at 19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023. Pafos. 19 June 2023 through 21 June 2023 (pp. 483-490). Institute of Electrical and Electronics Engineers Inc.
Öppna denna publikation i ny flik eller fönster >>Towards Cyber Threat Intelligence for the IoT
2023 (Engelska)Ingår i: Proceedings - 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023, Institute of Electrical and Electronics Engineers Inc. , 2023, s. 483-490Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

With the proliferation of digitization and its usage in critical sectors, it is necessary to include information about the occurrence and assessment of cyber threats in an organization’s threat mitigation strategy. This Cyber Threat Intelligence (CTI) is becoming increasingly important, or rather necessary, for critical national and industrial infrastructures. Current CTI solutions are rather federated and unsuitable for sharing threat information from low-power IoT devices. This paper presents a taxonomy and analysis of the CTI frameworks and CTI exchange platforms available today. It proposes a new CTI architecture relying on the MISP Threat Intelligence Sharing Platform customized and focusing on IoT environment. The paper also introduces a tailored version of STIX (which we call tinySTIX), one of the most prominent standards adopted for CTI data modeling, optimized for low-power IoT devices using the new lightweight encoding and cryptography solutions. The proposed CTI architecture will be very beneficial for securing IoT networks, especially the ones working in harsh and adversarial environments. 

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers Inc., 2023
Nyckelord
Internet of things; Cybe threat intelligence; Cyber threats; Digitisation; Indicator of compromize; Low Power; MISP; Mitigation strategy; National infrastructure; STIX; Threats mitigations; Network architecture
Nationell ämneskategori
Data- och informationsvetenskap
Identifikatorer
urn:nbn:se:ri:diva-67676 (URN)10.1109/DCOSS-IoT58021.2023.00081 (DOI)2-s2.0-85174417452 (Scopus ID)
Konferens
19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023. Pafos. 19 June 2023 through 21 June 2023
Anmärkning

This work has been supported by the H2020 projectARCADIAN-IoT (https://www.arcadian-iot.eu/) [G.A. No.101020259] 

Tillgänglig från: 2023-11-14 Skapad: 2023-11-14 Senast uppdaterad: 2023-11-14Bibliografiskt granskad
Figueiredo, S., Silva, P., Iacovazzi, A., Holubenko, V., Casal, J., Calero, J. M., . . . Raza, S. (2022). ARCADIAN-IoT - Enabling Autonomous Trust, Security and Privacy Management for IoT. In: Lect. Notes Comput. Sci. 5th The Global IoT Summit, GIoTS 2022. Dublin 20 June 2022 through 23 June 2022: . Paper presented at 5th The Global IoT Summit, GIoTS 2022. Dublin 20 June 2022 through 23 June 2022 (pp. 348-359). Springer Science and Business Media Deutschland GmbH, 13533
Öppna denna publikation i ny flik eller fönster >>ARCADIAN-IoT - Enabling Autonomous Trust, Security and Privacy Management for IoT
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2022 (Engelska)Ingår i: Lect. Notes Comput. Sci. 5th The Global IoT Summit, GIoTS 2022. Dublin 20 June 2022 through 23 June 2022, Springer Science and Business Media Deutschland GmbH , 2022, Vol. 13533, s. 348-359Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Cybersecurity incidents have been growing both in number and associated impact, as a result from society’s increased dependency in information and communication technologies - accelerated by the recent pandemic. In particular, IoT. technologies, which enable significant flexibility and cost-efficiency, but are also associated to more relaxed security mechanisms, have been quickly adopted across all sectors of the society, including critical infrastructures (e.g. smart grids) and services (e.g. eHealth). Gaps such as high dependence on 3rd party IT suppliers and device manufacturers increase the importance of trustworthy and secure solutions for future digital services. This paper presents ARCADIAN-IoT, a framework aimed at holistically enabling trust, security, privacy and recovery in IoT systems, and enabling a Chain of Trust between the different IoT entities (persons, objects and services). It builds on features such as federated AI for effective and privacy-preserving cybersecurity, distributed ledger technologies for decentralized management of trust, or transparent, user-controllable and decentralized privacy. © 2022, The Author(s)

Ort, förlag, år, upplaga, sidor
Springer Science and Business Media Deutschland GmbH, 2022
Nyckelord
ARCADIAN-IoT, Cybersecurity, IoT, Trust, Distributed ledger, Internet of things, Privacy-preserving techniques, Cost-efficiency, Cyber security, Information and Communication Technologies, Privacy management, Security and privacy, Security management, Trust management
Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:ri:diva-64111 (URN)10.1007/978-3-031-20936-9_28 (DOI)2-s2.0-85147849817 (Scopus ID)9783031209352 (ISBN)
Konferens
5th The Global IoT Summit, GIoTS 2022. Dublin 20 June 2022 through 23 June 2022
Tillgänglig från: 2023-02-28 Skapad: 2023-02-28 Senast uppdaterad: 2023-06-08Bibliografiskt granskad
Iacovazzi, A. & Raza, S. (2022). Ensemble of Random and Isolation Forests for Graph-Based Intrusion Detection in Containers. In: Proceedings of the 2022 IEEE International Conference on Cyber Security and Resilience, CSR 2022: . Paper presented at 2nd IEEE International Conference on Cyber Security and Resilience, CSR 2022, 27 July 2022 through 29 July 2022 (pp. 30-37). Institute of Electrical and Electronics Engineers Inc.
Öppna denna publikation i ny flik eller fönster >>Ensemble of Random and Isolation Forests for Graph-Based Intrusion Detection in Containers
2022 (Engelska)Ingår i: Proceedings of the 2022 IEEE International Conference on Cyber Security and Resilience, CSR 2022, Institute of Electrical and Electronics Engineers Inc. , 2022, s. 30-37Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

We propose a novel solution combining supervised and unsupervised machine learning models for intrusion detection at kernel level in cloud containers. In particular, the proposed solution is built over an ensemble of random and isolation forests trained on sequences of system calls that are collected at the hosting machine's kernel level. The sequence of system calls are translated into a weighted and directed graph to obtain a compact description of the container behavior, which is given as input to the ensemble model. We executed a set of experiments in a controlled environment in order to test our solution against the two most common threats that have been identified in cloud containers, and our results show that we can achieve high detection rates and low false positives in the tested attacks. 

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers Inc., 2022
Nyckelord
Cloud containers, Intrusion Detection System, Machine learning on Graph, Directed graphs, Forestry, Graphic methods, Intrusion detection, Machine learning, Cloud container, Graph-based, Intrusion Detection Systems, Intrusion-Detection, Machine-learning, Novel solutions, Supervised machine learning, System calls, Unsupervised machine learning, Containers
Nationell ämneskategori
Data- och informationsvetenskap
Identifikatorer
urn:nbn:se:ri:diva-60157 (URN)10.1109/CSR54599.2022.9850307 (DOI)2-s2.0-85137367814 (Scopus ID)9781665499521 (ISBN)
Konferens
2nd IEEE International Conference on Cyber Security and Resilience, CSR 2022, 27 July 2022 through 29 July 2022
Anmärkning

 Funding text 1: This research is partially funded by the EU H2020 ARCADIAN-IoT (Grant ID. 101020259) and partly by the H2020 CONCORDIA (Grant ID. 830927).

Tillgänglig från: 2022-10-10 Skapad: 2022-10-10 Senast uppdaterad: 2023-06-08Bibliografiskt granskad
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0001-6116-164X

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