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  • 1.
    Khan, Suleman
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
    Gaba, Gurjot Singh
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
    Braeken, An
    Vrije Universiteit Brussel, Brussels, Belgium.
    Kumar, Pardeep
    Swansea University, Swansea, UK.
    Gurtov, Andrei
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
    AKAASH: A realizable authentication, key agreement, and secure handover approach for controller-pilot data link communications2023In: International Journal of Critical Infrastructure Protection, ISSN 1874-5482, E-ISSN 2212-2087, Vol. 42, article id 100619Article in journal (Refereed)
    Abstract [en]

    Controller-Pilot Data Link Communications (CPDLC) are rapidly replacing voice-based Air Traffic Control (ATC) communications worldwide. Being digital, CPDLC is highly resilient and bandwidth efficient, which makes it the best choice for traffic-congested airports. Although CPDLC initially seems to be a perfect solution for modern-day ATC operations, it suffers from serious security issues. For instance, eavesdropping, spoofing, man-in-the-middle, message replay, impersonation attacks, etc. Cyber attacks on the aviation communication network could be hazardous, leading to fatal aircraft incidents and causing damage to individuals, service providers, and the aviation industry. Therefore, we propose a new security model called AKAASH, enabling several paramount security services, such as efficient and robust mutual authentication, key establishment, and a secure handover approach for the CPDLC-enabled aviation communication network. We implement the approach on hardware to examine the practicality of the proposed approach and verify its computational and communication efficiency and efficacy. We investigate the robustness of AKAASH through formal (proverif) and informal security analysis. The analysis reveals that the AKAASH adheres to the CPDLC standards and can easily integrate into the CPDLC framework.

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  • 2.
    Lin, Chih-Yuan
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Nadjm-Tehrani, Simin
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Protocol study and anomaly detection for server-driven traffic in SCADA networks2023In: International Journal of Critical Infrastructure Protection, ISSN 1874-5482, E-ISSN 2212-2087, Vol. 42, article id 100612Article in journal (Refereed)
    Abstract [en]

    Attacks against Supervisory Control and Data Acquisition (SCADA) systems operating critical infrastructures have largely appeared in the past decades. There are several anomaly detection systems that model the traffic of request-response mechanisms, where a client initiates a request to a server and the server sends back a response later. However, many modern SCADA protocols also allow server-driven traffic without a paired request, and anomaly detection for server-driven traffic has not been well-studied. This paper provides a comprehensive understanding of server-driven traffic across different protocols, such as MMS, Siemens S7, S7-plus, and IEC 60870-5-104 (IEC-104), with traffic analysis. The analysis results show that the common postulation of periodicity and correlation within SCADA traffic holds true for most of the analyzed datasets. The paper then proposes a Multivariate Correlation Anomaly Detection (MCAD) approach for server-driven traffic that presents complicated correlations among flows. The proposed approach is compared with a univariate correlation anomaly detection approach designed for SCADA and a general purpose anomaly detection approach based on neural network techniques. These approaches are tested with an IEC-104 dataset from a real power utility with injected timing perturbations resulting from a Stuxnet-like stealthy attack scenario. The detection accuracy of MCAD outperforms the compared methods and the time-to-detection performance is promising.

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