Cybersecurity has become an inevitable concern in the healthcare industry due to the rapid growth of the Internet of Health Things (IoHT). The IoHT is revolutionizing healthcare by enabling remote access to hospital equipment, real-time patient monitoring, and urgent alerts to patients and hospitals. However, the convenience of these systems also makes them vulnerable to cyberattacks, with hackers seeking to disrupt health services or extort money through ransomware attacks. Efficiently detecting multiple threats is a challenging task because IoHT generates large temporal data and system log information. In this article, we propose time-series classification models for the identification of potential cyberattacks in IoHT networks. First, we introduce neighborhood component analysis (NCA) with modifications of the regularization parameter to select the vital input features. With the selected features, we propose two LSTM-based models: 1) directed acyclic graph-based long short-term memory (DAG-LSTM) and 2) projected layer-based long short-term memory (PL-LSTM) for detecting cyberattacks. We evaluate the existing time-series classification models [i.e., gated recurrent unit (GRU), LSTM, and bilinear LSTM (Bi-LSTM)] and proposed models (i.e., DAG-LSTM and PL-LSTM) using real-world IoHT data. We also validate the models by applying a nonparametric statistical test, Friedman test. Our evaluation results show that the proposed DAG-LSTM achieves the highest accuracy with 99.89% training and 92.04% an average testing accuracy.

Large-scale high performance computing (HPC) systems typically consist of many thousands of CPUs and storage units, while used by hundreds to thousands of users at the same time. Applications from these large numbers of users have diverse characteristics, such as varying compute, communication, memory, and I/O intensiveness. A good understanding of the performance characteristics of each user application is important for job scheduling and resource provisioning. Among these performance characteristics, the I/O performance is difficult to predict because the I/O system software is complex, the I/O system is shared among all users, and the I/O operations also heavily rely on networking systems. To improve the prediction of the I/O performance on HPC systems, we propose to integrate information from a number of different system logs and develop a regression-based approach that dynamically selects the most relevant features from the most recent log entries, and automatically select the best regression algorithm for the prediction task. Evaluation results show that our proposed scheme can predict the I/O performance with up to 84% prediction accuracy in the case of the I/O-intensive applications using the logs from CORI supercomputer at NERSC.

The proliferation of the Internet of Health Things (IoHT) introduces significant benefits for healthcare through enhanced connectivity and data-driven insights, but it also presents substantial cybersecurity challenges. Protecting sensitive health data from cyberattacks is critical. This paper proposes a novel approach for detecting cyberattacks in IoHT environments using a Federated Learning (FL) framework integrated with Long Short-Term Memory (LSTM) networks. The FL paradigm ensures data privacy by allowing individual IoHT devices to collaboratively train a global model without sharing local data, thereby maintaining patient confidentiality. LSTM networks, known for their effectiveness in handling time-series data, are employed to capture and analyze temporal patterns indicative of cyberthreats. Our proposed system uses an embedded feature selection technique that minimizes the computational complexity of the cyberattack detection model and leverages the decentralized nature of FL to create a robust and scalable cyberattack detection mechanism. We refer to the proposed approach as Embedded Federated Learning-Driven Long Short-Term Memory (EFL-LSTM). Extensive experiments using real-world ECU-IoHT data demonstrate that our proposed model outperforms traditional models regarding accuracy (97.16%) and data privacy. The outcomes highlight the feasibility and advantages of integrating Federated Learning with LSTM networks to enhance the cybersecurity posture of IoHT infrastructures. This research paves the way for future developments in secure and privacy-preserving IoHT systems, ensuring reliable protection against evolving cyberthreats.

Large high-performance computers (HPC) are expensive tools responsible for supporting thousands of scientific applications. However, it is not easy to determine the best set of configurations for workloads to best utilize the storage and I/O systems. Users typically use the default configurations provided by the system administrators, which typically results in poor performance. In an effort to identify application characteristics more important to I/O performance, we applied several machine learning techniques to characterize these applications. To identify the features that are most relevant to the I/O performance, we evaluate a number of different feature selection methods, e.g., Mutual information regression and F regression, and develop a novel feature selection method based on Min-max mutual information. These feature selection methods allow us to sift through a large set of the real-world workloads collected from NERSC's Cori supercomputer system, and identify the most important features. We employ a number of different clustering algorithms, including KMeans, Gaussian Mixture Model (GMM) and Ward linkage, and measure the cluster quality with Davies Boulder Index (DBI), Silhouette and a new Combined Score developed for this work. The cluster evaluation result shows that the test dataset could be best divided into three clusters, where cluster 1 contains mostly small jobs with operations on standard I/O units, cluster 2 consists of middle size parallel jobs dominated by read operations, and cluster 3 include large parallel jobs with heavy write operations. The cluster characteristics suggest that using parallel I/O library MPI IO and a large number of parallel cores are important to achieve high I/O throughput.

Scientific applications frequently spend a large fraction of their execution time in reading and writing data on parallel file systems. Identifying these I/O performance bottlenecks and attributing root causes are critical steps toward devising optimization strategies. Several existing studies analyze I/O logs of a set of benchmarks or applications that were run with controlled behaviors. However, there is still a lack of general approach that systematically identifies I/O performance bottlenecks for applications running "in the wild" on production systems. In this study, we have developed an analysis approach of "zooming in" from platform-wide to application-wide to job-level I/O logs for identifying I/O bottlenecks in arbitrary scientific applications. We analyze the logs collected on a Cray XC40 system in production over a two-month period. This study results in several insights for application developers to use in optimizing I/O behavior.