DocumentCode :
187442
Title :
Using Invariants for Anomaly Detection: The Case Study of a SaaS Application
Author :
Frattini, Flavio ; Sarkar, Santonu ; Khasnabish, Jyotiska Nath ; Russo, S.
Author_Institution :
Univ. degli Studi di Napoli, Naples, Italy
fYear :
2014
fDate :
3-6 Nov. 2014
Firstpage :
383
Lastpage :
388
Abstract :
Invariants represent properties of a system that are expected to hold when everything goes well. Thus, the violation of an invariant most likely corresponds to the occurrence of an anomaly in the system. In this paper, we discuss the accuracy and the completeness of an anomaly detection system based on invariants. The case study we have taken is a back-end operation of a SaaS platform. Results show the rationality of the approach and discuss the impact of the invariant mining strategy on the detection capabilities, both in terms of accuracy and of time to reveal violations.
Keywords :
cloud computing; data mining; security of data; SaaS application; anomaly detection system; back-end operation; invariant mining strategy; system anomaly occurrence; system properties; Accuracy; Data mining; Detectors; Market research; Monitoring; Software as a service; Time series analysis; Anomaly detection; SaaS; invariants;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Software Reliability Engineering Workshops (ISSREW), 2014 IEEE International Symposium on
Conference_Location :
Naples
Type :
conf
DOI :
10.1109/ISSREW.2014.57
Filename :
6983871
Link To Document :
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