Evaluation of anomaly-based IDS for mobile devices using machine learning classifiers

Dimitrios Damopoulos; Sofia A. Menesidou; Georgios Kambourakis; Maria Papadaki; Nathan Clarke; Stefanos Gritzalis

doi:10.1002/sec.341

Evaluation of anomaly-based IDS for mobile devices using machine learning classifiers

Dimitrios Damopoulos^*
, Sofia A. Menesidou
, Georgios Kambourakis
, Maria Papadaki
, Nathan Clarke
, Stefanos Gritzalis

^*Corresponding author for this work

School of Engineering, Computing and Mathematics

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	3-14
Number of pages	0
Journal	Security and Communication Networks
Volume	5
Issue number	1
DOIs	https://doi.org/10.1002/sec.341
Publication status	Published - 2012

Keywords

mobile devices
anomaly-based intrusion detection system
user behaviour
machine learning classifiers

Access to Document

10.1002/sec.341

Cite this

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title = "Evaluation of anomaly-based IDS for mobile devices using machine learning classifiers",

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author = "Dimitrios Damopoulos and Menesidou, \{Sofia A.\} and Georgios Kambourakis and Maria Papadaki and Nathan Clarke and Stefanos Gritzalis",

year = "2012",

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issn = "1939-0114",

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AU - Damopoulos, Dimitrios

AU - Menesidou, Sofia A.

AU - Kambourakis, Georgios

AU - Papadaki, Maria

AU - Clarke, Nathan

AU - Gritzalis, Stefanos

PY - 2012

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KW - mobile devices

KW - anomaly-based intrusion detection system

KW - user behaviour

KW - machine learning classifiers

U2 - 10.1002/sec.341

DO - 10.1002/sec.341

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VL - 5

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JO - Security and Communication Networks

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