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Predictability of NetFlow data
- Source :
- ISI, IEEE International Conference on Intelligence and Security Informatics
- Publication Year :
- 2016
- Publisher :
- IEEE, 2016.
-
Abstract
- The behaviour of individual devices connected to an enterprise network can vary dramatically, as a device’s activity depends on the user operating the device as well as on all behind the scenes operations between the device and the network. Being able to understand and predict a device’s behaviour in a network can work as the foundation of an anomaly detection framework, as devices may show abnormal activity as part of a cyber attack. The aim of this work is the construction of a predictive regression model for a device’s behaviour at normal state. The behaviour of a device is presented by a quantitative response and modelled to depend on historic data recorded by NetFlow.
- Subjects :
- Technology
Computer science
Regression trees
Feature extraction
Principal component analysis
02 engineering and technology
computer.software_genre
01 natural sciences
Electronic mail
010104 statistics & probability
Engineering
Computer Science, Theory & Methods
Predictive regression
NetFlow
0202 electrical engineering, electronic engineering, information engineering
Enterprise private network
0101 mathematics
Predictability
Science & Technology
Engineering, Electrical & Electronic
Computer Science
Cyber-attack
020201 artificial intelligence & image processing
Anomaly detection
Data mining
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2016 IEEE Conference on Intelligence and Security Informatics (ISI)
- Accession number :
- edsair.doi.dedup.....d07f2f961d984150eaec0f6e27013421