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Over the past few years, the synergic usage of unmanned aerial vehicles (later drones) and Internet of Things (IoT) has successfully transformed into the Internet of Drones (IoD) paradigm, where the data of interest is gathered and delivered to the Zone Service Provider (ZSP) by drones for substantial additional analysis. Considering the sensitivity of collected information and the impact of information disclosure, information privacy and security issues should be resolved properly so that the maximum potential of IoD can be realized in the increasingly complex cyber threat environment. Ideally, an authentication and key agreement protocol can be adopted to establish secure communications between drones and the ZSP in an insecure environment. Nevertheless, a large group of drones authenticating with the ZSP simultaneously will lead to a severe authentication signaling congestion, which inevitably degrades the quality of service (QoS) of IoD systems. To properly address the above-mentioned issues, a lightweight group authentication protocol, called liteGAP, is proposed in this paper. liteGAP can achieve the authenticated key establishment between a group of drones and the ZSP concurrently in the IoD environment using lightweight operations such as hash function, bitwise XOR, and physical unclonable function (PUF). We verify liteGAP using AVISPA (a tool for the automatic verification of security protocols) and conduct formal and informal security analysis, proving that liteGAP meets all pre-defined security requirements and withstand various potential cyber attacks. Moreover, we develop an experimental framework and conduct extensive experiments on liteGAP and two benchmark schemes (e.g., GASE and rampIoD). Experimental findings show that liteGAP outperforms its counterparts in terms of computational cost as well as communication overhead. 

Abstract The purpose of this study is to identify additional clinical features for sepsis detection through the use of a novel mechanism for interpreting black-box machine learning models trained and to provide a suitable evaluation for the mechanism. We use the publicly available dataset from the 2019 PhysioNet Challenge. It has around 40,000 Intensive Care Unit (ICU) patients with 40 physiological variables. Using Long Short-Term Memory (LSTM) as the representative black-box machine learning model, we adapted the Multi-set Classifier to globally interpret the black-box model for concepts it learned about sepsis. To identify relevant features, the result is compared against: (i) features used by a computational sepsis expert, (ii) clinical features from clinical collaborators, (iii) academic features from literature, and (iv) significant features from statistical hypothesis testing. Random Forest was found to be the computational sepsis expert because it had high accuracies for solving both the detection and early detection, and a high degree of overlap with clinical and literature features. Using the proposed interpretation mechanism and the dataset, we identified 17 features that the LSTM used for sepsis classification, 11 of which overlaps with the top 20 features from the Random Forest model, 10 with academic features and 5 with clinical features. Clinical opinion suggests, 3 LSTM features have strong correlation with some clinical features that were not identified by the mechanism. We also found that age, chloride ion concentration, pH and oxygen saturation should be investigated further for connection with developing sepsis. Interpretation mechanisms can bolster the incorporation of state-of-the-art machine learning models into clinical decision support systems, and might help clinicians to address the issue of early sepsis detection. The promising results from this study warrants further investigation into creation of new and improvement of existing interpretation mechanisms for black-box models, and into clinical features that are currently not used in clinical assessment of sepsis.