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  • 1.
    Zendon, Ivo
    et al.
    RISE Research Institutes of Sweden.
    Wang, Han
    RISE Research Institutes of Sweden.
    Iacovazzi, Alfonso
    RISE Research Institutes of Sweden.
    Vahidi, Arash
    RISE Research Institutes of Sweden.
    Bolm, Rolf
    RISE Research Institutes of Sweden.
    Raza, Shahid
    RISE Research Institutes of Sweden.
    On the Resilience of Machine Learning-Based IDS for Automotive Networks2023Ingår i: 2023 IEEE Vehicular Networking Conference (VNC), Institute of Electrical and Electronics Engineers (IEEE), 2023, s. 239-246Konferensbidrag (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.

  • 2.
    Iacovazzi, Alfonso
    et al.
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Wang, Han
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Butun, Ismail
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Raza, Shahid
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Towards Cyber Threat Intelligence for the IoT2023Ingår 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 (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. 

  • 3.
    Zenden, Ivo
    et al.
    RISE Research Institutes of Sweden.
    Wang, Han
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Iacovazzi, Alfonso
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Vahidi, Arash
    RISE Research Institutes of Sweden.
    Blom, Rolf
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Raza, Shahid
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    On the Resilience of Machine Learning-Based IDS for Automotive Networks2023Ingår i: proc of IEEE Vehicular Networking Conference, VNC, IEEE Computer Society , 2023, s. 239-246Konferensbidrag (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.

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  • 4.
    Figueiredo, S.
    et al.
    Instituto Pedro Nunes, Portugal.
    Silva, P.
    Instituto Pedro Nunes, Portugal; University of Coimbra, Portugal.
    Iacovazzi, Alfonso
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Holubenko, V.
    Instituto Pedro Nunes, Portugal.
    Casal, J.
    SCNL Truphone SA, Portugal.
    Calero, J. M. A.
    University of the West of Scotland, UK.
    Wang, Q.
    University of the West of Scotland, UK.
    Colarejo, P.
    LOAD Interactive, Portugal.
    Armitt, R. L.
    ATOS, Spain.
    Inches, G.
    Martel Innovate, Switzerland.
    Raza, Shahid
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    ARCADIAN-IoT - Enabling Autonomous Trust, Security and Privacy Management for IoT2022Ingår 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 (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)

  • 5.
    Iacovazzi, Alfonso
    et al.
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Raza, Shahid
    RISE Research Institutes of Sweden, Digitala system, Datavetenskap.
    Ensemble of Random and Isolation Forests for Graph-Based Intrusion Detection in Containers2022Ingår 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 (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. 

  • 6.
    Wang, Han
    et al.
    RISE Research Institutes of Sweden.
    Iacovazzi, Alfonso
    RISE Research Institutes of Sweden.
    Kim, Seonghyun
    Ericsson AB.
    Raza, Shahid
    RISE Research Institutes of Sweden.
    MAS-CTI: Machine Learning Assisted System for Cyber Threat IntelligenceManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    Cyber Threat Intelligence (CTI) is a critical component of modern cybersecurity, providing organizations with essential information to detect, prevent, and respond to cyber threats. However, CTI data is often non-uniform, incomplete, and inconsistent, making it challenging to analyze and manage effectively. Machine Learning (ML) models offer a powerful solution to overcome these challenges, providing advanced tools for data processing, sharing, and analysis. In this paper, we present MAS-CTI, an extended version of the popular CTI platform MISP, leveraging the power of ML for CTI processing. In particular, we address three key challenges in the CTI domain: event type identification, threat ranking, and IoC correlation. Additionally, to address concerns regarding IoC confidentiality, we explore the application of Federated Learning (FL) for event identification. We have conducted extensive testing of the models on three public CTI datasets, and the results obtained demonstrate the potential of ML models to enhance CTI processing and analysis, with only a few exceptions. 

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