Biblio

Data generated from various dynamic applications of Internet of Things (IoT) based healthcare technology is effectively used for decision-making, providing reliable and smart healthcare services to the elderly and patients with chronic diseases. Since these precious data are susceptible to various security attacks, continuous monitoring of the system's compliance and identification of security risks in IoT data propagation is essential through potentially several layers of applications. This paper pinpoints how security-aware data provenance graphs can support compliance checking and risk estimation by including sufficient information on security controls and other security-relevant evidence. Real-time analysis of these security evidence to enable a step-wise validation and providing the evidence of this validation to end-users is currently not possible with the available data. This paper analyzes the security concerns in different phases of data propagation in a designed IoT-health scenario and promotes step-wise validation of security evidence. It proposes a system model with a novel protocol that documents and verifies evidence for security controls for data-object relations in data provenance graphs to assist compliance checking of security regulation of healthcare systems. With this regard, this paper discusses the proposed system model design with the requirements for technical safeguards of the Health Insurance Portability and Accountability Act (HIPAA). Based on the verification output at each phase, the proposed protocol reports this chain of verification by creating certain security tokens. Finally, the paper provides a formal security validation and security design analysis to show the applicability of this step-wise validation within the proposed system model.

A successful application of an Internet of Things (IoT) based network depends on the accurate and successful delivery of a large amount of data collected from numerous sources. However, the highly dynamic nature of IoT network prevents the establishment of clear security perimeters and hampers the understanding of security aspects. Risk assessment in such networks requires good situational awareness with respect to security. Therefore, a comprehensive view of data propagation including information on security controls can improve security analysis and risk assessment in each layer of data propagation in an IoT architecture. Documentation of metadata is already used in data provenance to identify who generates which data, how, and when. However, documentation of security information is not seen as relevant for data provenance graphs. In this paper, we discuss the importance of adding security metadata in a data provenance graph. We propose a novel IoT Provenance model, Prov-IoT, which documents the history of data records considering data processing and aggregation along with security metadata to enable a foundation for trust in data. The model portrays a comprehensive framework and outlines the identification of information to be included in designing a security-aware provenance graph. This can be beneficial for uncovering system fault or intrusion. Also, it can be useful for decision-based systems for security analysis and risk estimation. We design an associated class diagram for the Prov-IoT model. Finally, we use an IoT healthcare example scenario to demonstrate the impact of the proposed model.

Cybersecurity cannot be addressed by technology alone; the most intractable aspects are in fact sociotechnical. As a result, the 'human factor' has been recognised as being the weakest and most obscure link in creating safe and secure digital environments. This study examines the subjective and often complex nature of human factors in the cybersecurity context through a systematic literature review of 27 articles which span across technical, behavior and social sciences perspectives. Results from our study suggest that there is still a predominately a technical focus, which excludes the consideration of human factors in cybersecurity. Our literature review suggests that this is due to a lack of consolidation of the attributes pertaining to human factors; the application of theoretical frameworks; and a lack of in-depth qualitative studies. To ensure that these gaps are addressed, we propose that future studies take into consideration (a) consolidating the human factors; (b) examining cyber security from an interdisciplinary approach; (c) conducting additional qualitative research whilst investigating human factors in cybersecurity.