Venkatasubramanian, Krishna K.

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2012 - 20144
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  • Publication
    Functional Alarms for Systems of Interoperable Medical Devices
    (2014-01-09) Venkatasubramanian, Krishna; Vasserman, Eugene; Sokolsky, Oleg; Lee, Insup
    Alarms are essential for medical systems in order to ensure patient safety during deteriorating clinical situations and inevitable device malfunction. As medical devices are connected together to become interoperable, alarms become crucial part in making them high-assurance, in nature. Traditional alarm systems for interoperable medical devices have been patient-centric. In this paper, we introduce the need for an alarm system that focuses on the correct functionality of the interoperability architecture itself, along with several considerations and design challenges in enabling them.
  • Publication
    Security and Interoperable Medical Device Systems: Part 1
    (2012-09-01) Venkatasubramanian, Krishna K.; Vasserman, Eugene; Sokolsky, Oleg; Lee, Insup
    Interoperable medical devices (IMDs) face threats due to the increased attack surface presented by interoperability and the corresponding infrastructure. Introducing networking and coordination functionalities fundamentally alters medical systems' security properties. Understanding the threats is an important first step in eventually designing security solutions for such systems. Part 1 of this two-part article provides an overview of the IMD environment and the attacks that can be mounted on it.
  • Publication
    Security and Interoperable Medical Device Systems, Part 2: Failures, Consequences and Classifications
    (2012-11-01) Vasserman, Eugene; Venkatasubramanian, Krishna; Sokolsky, Oleg; Lee, Insup
    Interoperable medical devices (IMDs) face threats due to the increased attack surface presented by interoperability and the corresponding infrastructure. Introducing networking and coordination functionalities fundamentally alters medical systems' security properties. Understanding the threats is an important first step in eventually designing security solutions for such systems. Part 2 of this two-part article defines a failure model, or the specific ways in which IMD environments might fail when attacked. An attack-consequences model expresses the combination of failures experienced by IMD environments for each attack vector. This analysis leads to interesting conclusions about regulatory classes of medical devices in IMD environments subject to attacks.
  • Publication
    A Trust Model for Vehicular Network-Based Incident Reports
    (2013-06-02) Chang, Jian; Lee, Insup; Liao, Cong; Venkatasubramanian, Krishna K.
    Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) networks are ephemeral, short-duration wireless networks that have the potential to improve the overall driving experience through the exchange of information between vehicles. V2V and V2I networks operate primarily by distributing real-time incident reports regarding potential traffic problems such as traffic jams, accidents, bad roads and so on to other vehicles in their vicinity over a multi-hop network. However, given the presence of malicious entities, blindly trusting such incident reports (even the one received through a cryptographically secure channel) can lead to undesirable consequences. In this paper, we propose an approach to determine the likelihood of the accuracy of V2V incident reports based on the trustworthiness of the report originator and those vehicles that forward it. The proposed approach takes advantage of existing road-side units (RSU) based V2I communication infrastructure deployed and managed by central traffic authorities, which can be used to collect vehicle behavior information in a crowd-sourcedfashion for constructing a more comprehensive view of vehicle trustworthiness. For validating our scheme, we implemented a V2V/V2I trust simulator by extending an existing V2V simulator with trust management capabilities. Preliminary analysis of the model shows promising results. By combining our trust modeling technique with a threshold-based decision strategy, we observed on average 85% accuracy.