Improving Predictability of User-Affecting Metrics to Support Anomaly Detection in Cloud Services

Improving Predictability of User-Affecting Metrics to Support Anomaly Detection in Cloud Services

Anomaly detection systems aim to detect and report attacks or unexpected behavior in networked systems. Previous work has shown that anomalies have an impact on system performance, and that performance signatures can be effectively used for implementing an IDS. In this paper, we present an analytica...

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Bibliographic Details
Main Authors: Vilc Queupe Rufino, Mateus Schulz Nogueira, Alberto Avritzer, Daniel Sadoc Menasche, Barbara Russo, Andrea Janes, Vincenzo Ferme, Andre Van Hoorn, Henning Schulz, Cabral Lima
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Computer security
security management
anomaly detection
performance evaluation
queueing theory
analytical model
Online Access:https://ieeexplore.ieee.org/document/9212393/

Internet

https://ieeexplore.ieee.org/document/9212393/

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