Structural temporal graph neural networks for anomaly detection in dynamic graphs
Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications
in areas such as security, finance, and social media. Existing network embedding based …
in areas such as security, finance, and social media. Existing network embedding based …
A synergistic approach for graph anomaly detection with pattern mining and feature learning
Detecting anomalies on graph data has two types of methods. One is pattern mining that
discovers strange structures globally such as quasi-cliques, bipartite cores, or dense blocks …
discovers strange structures globally such as quasi-cliques, bipartite cores, or dense blocks …
Multi-scale one-class recurrent neural networks for discrete event sequence anomaly detection
Discrete event sequences are ubiquitous, such as an ordered event series of process
interactions in Information and Communication Technology systems. Recent years have …
interactions in Information and Communication Technology systems. Recent years have …
MULAN: Multi-modal Causal Structure Learning and Root Cause Analysis for Microservice Systems
Effective root cause analysis (RCA) is vital for swiftly restoring services, minimizing losses,
and ensuring the smooth operation and management of complex systems. Previous data …
and ensuring the smooth operation and management of complex systems. Previous data …
Glad: Content-aware dynamic graphs for log anomaly detection
Logs play a crucial role in system monitoring and debugging by recording valuable system
information, including events and states. Although various methods have been proposed to …
information, including events and states. Although various methods have been proposed to …
Multi-modal Causal Structure Learning and Root Cause Analysis
Effective root cause analysis (RCA) is vital for swiftly restoring services, minimizing losses,
and ensuring the smooth operation and management of complex systems. Previous data …
and ensuring the smooth operation and management of complex systems. Previous data …
Higher-order Markov Graph based Bug Detection in Cloud-based Deployments
Detecting execution anomalies is an integral part of building and protecting modern large-
scale distributed systems. These systems generate a large volume of system logs to record …
scale distributed systems. These systems generate a large volume of system logs to record …
The Industry 4.0 for Secure and Smarter Manufacturing
NSG Ganesh, NGM Venkatesh - Advancing Smarter and More …, 2022 - igi-global.com
Industry 4.0 and smart manufacturing are expected to transform current practices into new
milestones of exponential growth with high intensity of velocity, scope, and system impact …
milestones of exponential growth with high intensity of velocity, scope, and system impact …
Autonomous UAV Edge Architecture for Road Hazards Extended Reality Warnings
Unmanned Aerial Vehicles (UAV) can be exploited to collect important information about
road hazards to be broadcast to people in the vicinity and steer clear of the event area …
road hazards to be broadcast to people in the vicinity and steer clear of the event area …
Guest Editorial: Non-IID Outlier Detection in Complex Contexts
Outlier detection, also known as anomaly detection, aims at identifying data instances that
are rare or significantly different from the majority of instances. Due to its significance in …
are rare or significantly different from the majority of instances. Due to its significance in …