FEDGEN Testbed: A Federated Genomics Private Cloud Infrastructure for Precision Medicine and Artificial Intelligence Research

The cloud computing space is enjoying a renaissance. Not long ago, cloud computing was confined to the wall of high-revenue companies, but in recent times a growing number of businesses, public and private institutions are turning to the cloud computing platform to reap the benefits of a self-service, scalable, and flexible infrastructure. Moreover, with the increased implementation, advantages, and popularity of artificial intelligence, the demand for computing environments to solve age-old problems such as malaria and cancer is on the rise. This paper presents the implementation of a cloud computing infrastructure, the FEDerated GENomics (FEDGEN) Testbed, to provide an adequate IT environment for cancer and malaria researchers. The cloud computing environment is built using Openstack middleware. OpenStack is deployed using Metal-As-A-Service (MAAS) and Juju. Virtual Machines (Instances) were deployed, and services (JupiterHub) were installed on the FEDGEN testbed. The built infrastructure would allow the running of models requiring high computing power and would allow for collaboration among teams

Monitoring and resource management taxonomy in interconnected cloud infrastructures: a survey

Cloud users have recently expanded dramatically. The cloud service providers (CSPs) have also increased and have therefore made their infrastructure more complex. The complex infrastructure needs to be distributed appropriately to various users. Also, the advances in cloud computing have led to the development of interconnected cloud computing environments (ICCEs). For instance, ICCEs include the cloud hybrid, intercloud, multi-cloud, and federated clouds. However, the sharing of resources is not facilitated by specific proprietary technologies and access interfaces used by CSPs. Several CSPs provide similar services but have different access patterns. Data from various CSPs must be obtained and processed by cloud users. To ensure that all ICCE tenants (users and CSPs) benefit from the best CSPs, efficient resource management was suggested. Besides, it is pertinent that cloud resources be monitored regularly. Cloud monitoring is a service that works as a third-party entity between customers and CSPs. This paper discusses a complete cloud monitoring survey in ICCE, focusing on cloud monitoring and its significance. Several current open-source monitoring solutions are discussed. A taxonomy is presented and analyzed for cloud resource management. This taxonomy includes resource pricing, assignment of resources, exploration of resources, collection of resources, and disaster management

ANOMALY-BASED INTRUSION DETECTION FOR A VEHICLE CAN BUS: A CASE FOR HYUNDAI AVANTE CN7

Flooding, spoofing, replay, and fuzzing are common in various types of attacks faced by enterprises and various network systems. In-vehicle network systems are not immune to attacks and threats. Intrusion detection systems using different algorithms are proposed to enhance the security of the in-vehicle network. We use a dataset provided and collected in "Car Hacking: Attack and Defense Challenge" during 2020. This dataset has been realized by the organizers of the challenge for security researchers. With the aid of this dataset, the work aimed to develop attack and detection techniques of Controller Area Network (CAN) using different algorithms such as support vector machine and Feedforward Neural Network. This research work also provides a comparison of the rendering of these algorithms. Based on experimental results, this work will help future researchers to benchmark their results for the given dataset. The results obtained in this work show that the model selection does not depend only on the model's accuracy that is explained by the accuracy paradox. Therefore, for the overall result accuracy of 62.65%, they show that the support vector machine presents the most satisfying output in terms of precision and recall. The Radial basis kernel gives 65% and 67% precision for fuzzing and flooding and the recall of 64% and 100% for replay and spoofing, respectively

ANOMALY-BASED INTRUSION DETECTION FOR A VEHICLE CAN BUS: A CASE FOR HYUNDAI AVANTE CN7

Flooding, spoofing, replay, and fuzzing are common in various types of attacks faced by enterprises and various network systems. In-vehicle network systems are not immune to attacks and threats. Intrusion detection systems using different algorithms are proposed to enhance the security of the in-vehicle network. We use a dataset provided and collected in "Car Hacking: Attack and Defense Challenge" during 2020. This dataset has been realized by the organizers of the challenge for security researchers. With the aid of this dataset, the work aimed to develop attack and detection techniques of Controller Area Network (CAN) using different algorithms such as support vector machine and Feedforward Neural Network. This research work also provides a comparison of the rendering of these algorithms. Based on experimental results, this work will help future researchers to benchmark their results for the given dataset. The results obtained in this work show that the model selection does not depend only on the model's accuracy that is explained by the accuracy paradox. Therefore, for the overall result accuracy of 62.65%, they show that the support vector machine presents the most satisfying output in terms of precision and recall. The Radial basis kernel gives 65% and 67% precision for fuzzing and flooding and the recall of 64% and 100% for replay and spoofing, respectively

A Review of Evolutionary Trends in Cloud Computing and Applications to the Healthcare Ecosystem

Cloud computing is a technology that allows dynamic and flexible computing capability and storage through on-demand delivery and pay-as-you-go services over the Internet. %is technology has brought significant advances in the Information Technology (IT) domain. In the last few years, the evolution of cloud computing has led to the development of new technologies such as cloud federation, edge computing, and fog computing. However, with the development of Internet of %ings (IoT), several challenges have emerged with these new technologies. %erefore, this paper discusses each of the emerging cloud-based technologies, as well as their architectures, opportunities, and challenges. We present how cloud computing evolved from one paradigm to another through the interplay of benefits such as improvement in computational resources through the combination of the strengths of various Cloud Service Providers (CSPs), decrease in latency, improvement in bandwidth, and so on. Furthermore, the paper highlights the application of different cloud paradigms in the healthcare ecosyste

FEDARGOS-V1: A Monitoring Architecture for Federated Cloud Computing Infrastructures

Resource management in cloud infrastructure is one of the key elements of quality of services provided by the cloud service providers. Resource management has its taxonomy, which includes discovery of resources, selection of resources, allocation of resources, pricing of resources, disaster management, and monitoring of resources. Specifically, monitoring provides the means of knowing the status and availability of the physical resources and services within the cloud infrastructure. This results in making “monitoring of resources” one of the key aspects of the cloud resource management taxonomy. However, managing the resources in a secure and scalable manner is not easy, particularly when considering a federated cloud environment. A federated cloud is used and shared by many multi-cloud tenants and at various cloud software stack levels. As a result, there is a need to reconcile all the tenants’ diverse monitoring requirements. To cover all aspects relating to the monitoring of resources in a federated cloud environment, we present the FEDerated Architecture for Resource manaGement and mOnitoring in cloudS Version 1.0 (FEDARGOS-V1), a cloud resource monitoring architecture for federated cloud infrastructures. The architecture focuses mainly on the ability to access information while monitoring services for early identification of resource constraints within short time intervals in federated cloud platforms. The monitoring architecture was deployed in a real-time OpenStack-based FEDerated GENomic (FEDGEN) cloud testbed. We present experimental results in order to evaluate our design and compare it both qualitatively and quantitatively to a number of existing Cloud monitoring systems that are similar to ours. The architecture provided here can be deployed in private or public federated cloud infrastructures for faster and more scalable resource monitoring