With the rapid development of emerging technologies such as self-media, the Internet of
Things, and cloud computing, massive data applications are crossing the threshold of the …

With over 4.57 billion people using the Internet in 2020, the amount of data being generated
has exceeded 2.5 quintillion bytes per day. This rapid increase in the generation of data has …

The use of system logs for detecting and troubleshooting anomalies of production systems
has been known since the early days of computers. In spite of the advances in the area, the …

The lack of effective defense resource allocation strategies and reliable multi-agent
collaboration mechanisms lead to the low stability of Deep Reinforcement Learning (DRL) …

This paper describes a study on log mining in the domain of microservices technologies. We
focus on the detection of anomalies from logs, ie, events requiring deeper inspection by …

Artificial intelligence and machine learning have recently made outstanding contributions to
the performance of information system and cyber--physical system security. There has been …

Context Recent years have seen a lot of attention into Deep Learning (DL) techniques used
to detect cybersecurity attacks. DL techniques can swiftly analyze massive datasets, and …

Due to the complexity of modern IT services, failures can be manifold, occur at any stage,
and are hard to detect. For this reason, anomaly detection applied to monitoring data such …

Zero trust architecture (ZTA) is a paradigm shift in how we protect data, stay connected and
access resources. ZTA is non-perimeter-based defence, which has been emerging as a …

Effective log anomaly detection can help operators locate and solve problems quickly,
ensure the rapid recovery of the system, and reduce economic losses. However, recent log …