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Security is a major concern when building large-scale computer systems. Cloud services have made it easier to provision large-scale systems on demand over the Internet. While the cloud service providers provide the required building blocks such as compute units, database servers, and storage, customers are still responsible for securely combining these systems to satisfy their organization’s security policy. The secure development and operation of such large-scale systems present technical challenges. Composing a larger system using components with known security properties that satisfy a given security policy without re-analyzing the individual components is a difficult problem. In this study, we attempted to analyze the composability of components from a security perspective using first-order predicate logic. We posit that if we build a system using individual components that satisfy a security policy, the composed system will be sound with regard to that policy. Additionally, the methodology can be used to identify drifts or violations during future changes in the system by running checks during the system release cycles for continuous verification.

The rapid proliferation of embedded devices has led to the growth of the Internet of Things (IoT) with applications in numerous domains such as home automation, healthcare, education and agriculture. However, many of the connected devices particularly in smart homes are the target of attacks that try to exploit security vulnerabilities such as hard-coded passwords and insecure data transfer. Recent studies show that there is a considerable surge in the number of phishing attacks targeting smart homes during the COVID-19 pandemic. Moreover, many of the existing user authentication protocols in the literature incur additional computational overhead and need to be made more resilient to smart home targeted attacks. In this paper, we propose a novel lightweight and privacy-preserving remote user authentication protocol for securing smart home applications. Our approach is based on Photo Response Non-Uniformity (PRNU) to make our protocol resilient to smart home attacks such as smartphone capture attacks and phishing attacks. In addition, the lightweight nature of our solution is suitable for deployment on heterogeneous and resource constrained IoT devices. Besides, we leverage geometric secret sharing for establishing mutual authentication among the participating entities. We validate the security of the proposed protocol using the AVISPA formal verification tool and prototype it on a Raspberry Pi to analyze the power consumption. Finally, a comparison with existing schemes reveals that our scheme incurs a 20% reduction in communication overhead on smart devices. Furthermore, our proposed scheme is usable as it absolves users from memorizing passwords and carrying smart cards.

The COVID-19 pandemic is an unprecedented global emergency. Clinicians and medical researchers are suddenly thrown into a situation where they need to keep up with the latest and best evidence for decision-making at work in order to save lives and develop solutions for COVID-19 treatments and preventions. However, a challenge is the overwhelming numbers of online publications with a wide range of quality. We explain a science gateway platform designed to help users to filter the overwhelming amount of literature efficiently (with speed) and effectively (with quality), to find answers to their scientific questions. It is equipped with a chatbot to assist users to overcome infodemic, low usability, and high learning curve. We argue that human-machine communication via a chatbot play a critical role in enabling the diffusion of innovations.

Abstract Understanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.

The rapid growth of COVID-19 publications has driven clinical researchers and healthcare professionals in pursuit to reduce the knowledge gap on reliable information for effective pandemic solutions. The manual task of retrieving high-quality publications based on the evidence pyramid levels, however, presents a major bottleneck in researchers’ workflows. In this paper, we propose an “evidence-based” recommender system namely, KnowCOVID-19 that utilizes an edge computing service to integrate recommender modules for data analytics using end-user thin-clients. The edge computing service features chatbot-based web interface that handles a given COVID-19 publication dataset using two recommender system modules: (i) evidence-based filtering that observes domain specific topics across the literature and classifies the filtered information according to a clinical category, and (ii) social filtering that allows diverse experts with similar objectives to collaborate via a “social plane” to jointly find answers to critical clinical questions to fight the pandemic. We compare the Domain-specific Topic Model (DSTM) used in our evidence-based filtering with state-of-the-art models considering the CORD-19 dataset (a COVID-19 publication archive) and show improved generalization effectiveness as well as knowledge pattern query effectiveness. In addition, we conduct a comparison study between a manual literature review process and the KnowCOVID-19 augmented process, and evaluate the benefits of our information retrieval techniques over important queries provided by COVID-19 clinical experts.

Patients are becoming aware of the importance of taking secure control and managing access over their medical data, thereby leading to the rise in the adoption of personal health record (PHR) systems. However, today's PHR systems fall short in providing secure and trustable data sharing and access facilities to patients when they are in emergency situations or temporarily incapacitated. Also, the existing PHR systems are centralized and vulnerable to the single point of failure problem. Integrating PHR systems with blockchain technology can help to overcome such limitations. In this paper, we propose a blockchain-based PHR architecture that employs smart contracts to implement multi-party authorization (MPA) and threshold cryptographic schemes to automate secure and trustable medical data sharing and access in PHR systems. Moreover, we mitigate the limited storage and computation capabilities of blockchain by using InterPlanetary File System (IPFS) storage and reputation-governed trusted oracles into the proposed architecture. MPA and threshold cryptographic schemes allow the patient to split and share a secret key with a set of trusted parties, such as the healthcare regulatory agency, guardians, and hospitals, in such a way that they can collectively decide on sharing medical data on behalf of patients. We present algorithms along with their full smart contract function implementation details. We evaluate the robustness and performance of our solution by performing correctness verification and cost analysis. Furthermore, we evaluate the proposed approach in terms of security, generalization, and limitation aspects to find out its feasibility and practicality. We make our smart contract code publicly available on GitHub.

Cloud-hosted services are being increasingly used in hosting business and scientific applications due to cost-effectiveness, scalability, and ease of deployment. To facilitate rapid development, change and release process of cloud-hosted applications, the area of development and operations (DevOps) is fast evolving. It is necessary to train the future generation of scientific application development professionals such that they are knowledgeable in the DevOps-enabled continuous integration/delivery automation. In this article, we present the design and development of our "Mizzou Cloud DevOps platform," an online self-service platform to learn cutting-edge Cloud DevOps tools/technologies using open/public cloud infrastructures for wide adoption amongst instructors/students. Our learning platform features scalability, flexibility, and extendability in providing Cloud DevOps concepts knowledge and hands-on skills. We detail our "application-inspired learning" methodology that is based on integration of real-world application use cases in eight learning modules that include laboratory exercises and self-study activities. The learning modules allow students to gain skills in using latest technologies (e.g., containerization, cluster and edge computing, data pipeline automation) to implement relevant security, monitoring, and adaptation mechanisms. The evaluation of our platform features a knowledge growth study to assess student learning, followed by a usability study to assess the online learning platform, as well as the curriculum content as perceived by instructors and students across multiple hands-on workshops.

Internet television (IPTV) is rapidly gaining popularity and is being widely deployed in content delivery networks on the Internet. In order to proactively deliver optimum user quality of experience (QoE) for IPTV, service providers need to identify network bottlenecks in real time. In this paper, we develop psycho-acoustic-visual models that can predict user QoE of multimedia applications in real time based on online network status measurements. Our models are neural network based and cater to multi-resolution IPTV applications that include QCIF, QVGA, SD, and HD resolutions encoded using popular audio and video codec combinations. On the network side, our models account for jitter and loss levels, as well as router queuing disciplines: packet-ordered and time-ordered FIFO. We evaluate the performance of our multi-resolution multimedia QoE models in terms of prediction characteristics, accuracy, speed, and consistency. Our evaluation results demonstrate that the models are pertinent for real-time QoE monitoring and resource adaptation in IPTV content delivery networks.