A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems

Anomaly detection is a crucial task in complex distributed systems. A thorough understanding of the requirements and challenges of anomaly detection is pivotal to the security of such systems, especially for real-world deployment. While there are many works and application domains that deal with this problem, few have attempted to provide an in-depth look at such systems. In this survey, we explore the potentials of graph-based algorithms to identify anomalies in distributed systems. These systems can be heterogeneous or homogeneous, which can result in distinct requirements. One of our objectives is to provide an in-depth look at graph-based approaches to conceptually analyze their capability to handle real-world challenges such as heterogeneity and dynamic structure. This study gives an overview of the State-of-the-Art (SotA) research articles in the field and compare and contrast their characteristics. To facilitate a more comprehensive understanding, we present three systems with varying abstractions as use cases. We examine the specific challenges involved in anomaly detection within such systems. Subsequently, we elucidate the efficacy of graphs in such systems and explicate their advantages. We then delve into the SotA methods and highlight their strength and weaknesses, pointing out the areas for possible improvements and future works.Comment: The first two authors (A. Danesh Pazho and G. Alinezhad Noghre) have equal contribution. The article is accepted by IEEE Transactions on Knowledge and Data Engineerin

Antiviral drug screen identifies DNA-damage response inhibitor as potent blocker of SARS-CoV-2 replication.

SARS-CoV-2 has currently precipitated the COVID-19 global health crisis. We developed a medium-throughput drug-screening system and identified a small-molecule library of 34 of 430 protein kinase inhibitors that were capable of inhibiting the SARS-CoV-2 cytopathic effect in human epithelial cells. These drug inhibitors are in various stages of clinical trials. We detected key proteins involved in cellular signaling pathways mTOR-PI3K-AKT, ABL-BCR/MAPK, and DNA-damage response that are critical for SARS-CoV-2 infection. A drug-protein interaction-based secondary screen confirmed compounds, such as the ATR kinase inhibitor berzosertib and torin2 with anti-SARS-CoV-2 activity. Berzosertib exhibited potent antiviral activity against SARS-CoV-2 in multiple cell types and blocked replication at the post-entry step. Berzosertib inhibited replication of SARS-CoV-1 and the Middle East respiratory syndrome coronavirus (MERS-CoV) as well. Our study highlights key promising kinase inhibitors to constrain coronavirus replication as a host-directed therapy in the treatment of COVID-19 and beyond as well as provides an important mechanism of host-pathogen interactions