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A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection.
Maintainable Log Datasets for Evaluation of Intrusion Detection Systems
For scalable testbed deployment, we use concepts from model-driven engineering that enable automatic generation and labeling of an arbitrary number of datasets that comprise repetitions of attack executions with variations of parameters.
Cryptography and Security
Runtime Analysis of Whole-System Provenance
Identifying the root cause and impact of a system intrusion remains a foundational challenge in computer security.
Cryptography and Security Operating Systems
Introducing a New Alert Data Set for Multi-Step Attack Analysis
To illustrate the potential of our data set, we experiment with alert prioritization as well as two open-source tools for meta-alert generation and attack graph extraction.
Cryptography and Security
Behavioural Reports of Multi-Stage Malware
The extensive damage caused by malware requires anti-malware systems to be constantly improved to prevent new threats.
Explainable Anomaly Detection for Industrial Control System Cybersecurity
Industrial Control Systems (ICSs) are becoming more and more important in managing the operation of many important systems in smart manufacturing, such as power stations, water supply systems, and manufacturing sites.
Practical Whole-System Provenance Capture
We also show how CamFlow can be leveraged to capture meaningful provenance without modifying existing applications.
Cryptography and Security
Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection
Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and to delay further progression.
Sparse Bayesian approach for metric learning in latent space
Also, the present work is extended for learning in the feature space induced by an RKHS kernel.
Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput Computing
A dataset for malware detection in Grid computing is described.
