Semi-automated discovery of application session structure
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Towards highly reliable enterprise network services via inference of multi-level dependencies
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
What's going on?: learning communication rules in edge networks
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Dependency detection using a fuzzy engine
DSOM'07 Proceedings of the Distributed systems: operations and management 18th IFIP/IEEE international conference on Managing virtualization of networks and services
Analysis of communities of interest in data networks
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Mining activity data for dynamic dependency discovery in e-business systems
IEEE Transactions on Network and Service Management
Hybridization of fuzzy GBML approaches for pattern classification problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Nowadays traffic monitoring and analysis tools provide poor information about traffic volume without giving any clear view of what the hidden rules and relationships that govern these flows are. Since the majority of flows is generated by services (web browsing, email, p2p) and most of these applications are dependent on many network assets (servers and databases) we should discover the underlying relationships of every application. We present a technique that discovers the hidden relationships among components of a network that consist of parts of specific applications. From time information and flow attributes, such as IP addresses and service ports, our method using a novel hybrid genetic algorithm produces a small set of fuzzy rules that can reveal the underlying relationships over a network without any guidance. These dependencies build a service graph which can become a useful tool for fault localization, monitoring service performance, designing changes and anomaly detection.