Nowadays, there is a huge and growing concern about security in information and
communication technology among the scientific community because any attack or anomaly …

Due to the sheer number of applications that uses computer networks, in which some are
crucial to users and enterprises, network management is essential. Therefore, integrity and …

Abstract Machine learning models are widely used for anomaly detection in network traffic.
Effective transformation of the raw traffic data into mathematical expressions and hyper …

Distributed denial of service (DDoS) attacks are a major threat to any network-based service
provider. The ability of an attacker to harness the power of a lot of compromised devices to …

The ever expanding the usage of cloud computing environments, connected applications
and Internet of Things-based devices have progressively increased the amount of data that …

It is remarkable how proactive network management is in such demand nowadays, since
networks are growing in size and complexity and Information Technology services cannot be …

Different techniques and methods have been widely used in the subject of automatic
anomaly detection in computer networks. Attacks, problems and internal failures when not …

This paper presents a new data-driven framework for unsupervised system-wide anomaly
detection for modern distributed complex systems within which there exists a strong …

Taking into account the accelerated rate of network growth, the occurrence of anomalies
becomes inevitable. A single anomaly can affect the network performance so it is crucial to …

This study presents the correlational paraconsistent machine (CPM), a tool for anomaly
detection that incorporates unsupervised models for traffic characterization and principles of …