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dc.contributor.authorChawla, Ashima
dc.contributor.authorLee, Brian
dc.contributor.authorFallon, Sheila
dc.contributor.authorJacob, Paul
dc.identifier.citationAshima Chawla, Brian Lee, Sheila Fallon, Paul Jacob (2019). Host based intrusion detection system with combined CNN/RNN model. EMCL PKDD 2018 Workshops. Nemesis 2018, UrbReas 2018, SoGood 2018, IWAISe 2018, and Green Data Mining 2018, Dublin, Ireland, September 10-14, 2018, Proceedings. (print ISBN) 9783030134525, (electronic ISBN) 9783030134532en_US
dc.identifier.otherSoftware Research Institute - Articlesen_US
dc.description.abstractCyber security has become one of the most challenging as- pects of modern world digital technology and it has become imperative to minimize and possibly avoid the impact of cybercrimes. Host based intrusion detection systems help to protect systems from various kinds of malicious cyber attacks. One approach is to determine normal behaviour of a system based on sequences of system calls made by processes in the system [1]. This paper describes a computational e cient anomaly based intrusion detection system based on Recurrent Neural Networks. Using Gated Recurrent Units rather than the normal LSTM networks it is possible to obtain a set of comparable results with reduced training times. The incorporation of stacked CNNs with GRUs leads to improved anomaly IDS. Intrusion Detection is based on determining the prob- ability of a particular call sequence occurring from a language model trained on normal call sequences from the ADFA Data set of system call traces [2]. Sequences with a low probability of occurring are class ed as an anomaly.en_US
dc.publisherSpringer International Publishingen_US
dc.relation.ispartofECML PKDD 2018 Workshopen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland*
dc.subjectComputer scienceen_US
dc.subjectComputers - Internet securityen_US
dc.subjectNeural networks (Computer science)en_US
dc.titleHost based intrusion detection system with combined CNN/RNN model.en_US
dc.typeBook chapteren_US
dc.rights.accessOpen Accessen_US
dc.subject.departmentSoftware Research Institute AITen_US

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland