DocumentCode :
2119946
Title :
Analysing Behaviours for Intrusion Detection
Author :
Mamalakis, George ; Diou, Christos ; Symeonidis, Andreas L.
Author_Institution :
School of Electrical and Computer Engineering, Aristotle Univ. of Thessaloniki, Greece
fYear :
2015
fDate :
8-12 June 2015
Firstpage :
2645
Lastpage :
2651
Abstract :
In this work, a Behaviour-based Intrusion Detection Model is suggested. The proposed model can be employed from a single host configuration to a distributed mixture of host-based and network-based Intrusion Detection Systems (IDSs). Unlike most state-of-the-art IDSs that rely on analysing lower-level, raw-data representations, our proposed architecture suggests to use higher-level notions -behaviours- instead; this way, the IDS is able to identify more sophisticated attacks. To assess our premise, a Behaviour-based IDS (BIDS) prototype has been designed and developed that scans file system data to identify attacks. BIDS achieves high detection rates with low corresponding false positive rates, superseding other state-of-the-art file system IDSs.
Keywords :
Clustering algorithms; Computers; Engines; Feature extraction; Generators; Internet of things; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communication Workshop (ICCW), 2015 IEEE International Conference on
Conference_Location :
London, United Kingdom
Type :
conf
DOI :
10.1109/ICCW.2015.7247578
Filename :
7247578
Link To Document :
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