Pattern and policy driven log analysis for software monitoring

Razavi, A; Kontogiannis, K

dc.contributor.author	Razavi, A	en
dc.contributor.author	Kontogiannis, K	en
dc.date.accessioned	2014-03-01T02:45:44Z
dc.date.available	2014-03-01T02:45:44Z
dc.date.issued	2008	en
dc.identifier.issn	07303157	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32348
dc.subject	Approximate Matching	en
dc.subject	Collaborative Application	en
dc.subject	Log Analysis	en
dc.subject	Software Industry	en
dc.subject	Software Systems	en
dc.subject	Trace Analysis	en
dc.subject	Multi User	en
dc.subject	viterbi algorithm	en
dc.subject.other	Computer applications	en
dc.subject.other	Computer software maintenance	en
dc.subject.other	Computers	en
dc.subject.other	Health	en
dc.subject.other	Risk assessment	en
dc.subject.other	Viterbi algorithm	en
dc.subject.other	Word processing	en
dc.subject.other	Approximate matching	en
dc.subject.other	Component-based	en
dc.subject.other	Diagnostic systems	en
dc.subject.other	Industrial software	en
dc.subject.other	Log analysis	en
dc.subject.other	Log data	en
dc.subject.other	Policy-driven	en
dc.subject.other	Software industry	en
dc.subject.other	Software monitoring	en
dc.subject.other	System maintenance	en
dc.subject.other	Threat modeling	en
dc.subject.other	Viterbi	en
dc.subject.other	Computer software	en
dc.title	Pattern and policy driven log analysis for software monitoring	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/COMPSAC.2008.81	en
heal.identifier.secondary	http://dx.doi.org/10.1109/COMPSAC.2008.81	en
heal.identifier.secondary	4591541	en
heal.publicationDate	2008	en
heal.abstract	The component-based nature of large industrial software systems that consist of a number of diverse collaborating applications, pose significant challenges with respect to system maintenance, monitoring, auditing, and diagnosing. In this context, a monitoring and diagnostic system interprets log data to recognize patterns of significant events that conform to specific Threat Models. Threat Models have been used by the software industry for analyzing and documenting a system's risks in order to understand a system's threat profile. In this paper, we propose a framework whereby patterns of significant events are represented as expressions of a specialized monitoring language that are used to annotate specific threat models. An approximate matching technique that is based on the Viterbi algorithm is then used to identify whether system generated events, fit the given patterns. The technique has been applied and evaluated considering threat models and monitoring policies in logs that have been obtained from multi-user MS-Windows© based systems. © 2008 IEEE.	en
heal.journalName	Proceedings - International Computer Software and Applications Conference	en
dc.identifier.doi	10.1109/COMPSAC.2008.81	en
dc.identifier.spage	108	en
dc.identifier.epage	111	en