Please use this identifier to cite or link to this item: http://hdl.handle.net/11189/6276
DC FieldValueLanguage
dc.contributor.authorBotes, FHen_US
dc.contributor.authorLeenen, Len_US
dc.contributor.authorDe la Harpe, Arethaen_US
dc.date.accessioned2018-05-07T08:36:16Z-
dc.date.available2018-05-07T08:36:16Z-
dc.date.issued2017-
dc.identifier.isbn978-1-911218-44-9-
dc.identifier.issn2048-8610-
dc.identifier.urihttp://hdl.handle.net/11189/6276-
dc.descriptionProceedings of the 16th European Conference on Cyber Warfare and Security (ECCWS 2017), Dublin, Ireland, 29 - 30 June 2017en_US
dc.description.abstractIn the ashes of Moore’s Law, companies have to acclimatise to the vast increase of data flowing through their networks. Reports on information breaches and hackers claiming ransom for company data are rampant. We live in a world where data requirements have become dynamic, where things are constantly changing. The field of intrusion detection however have not changed much, traditional detection methods are still the norm for commercial products promoting a rigid, manual and static detection platform. Intrusion Detection Systems (IDS) analyse network traffic to identify suspicious patterns with the intention to compromise the system. Practitioners train classifiers to classify the data within different categories e.g. malicious or normal network traffic. Machine learning has great potential when applied in the intrusion detection domain: decision trees (DT), random forests (RF) and ant colony optimization (ACO) are all popular research topics. This paper focuses on the recent advances within machine learning, specifically the Ant Tree Miner (ATM) classifier. The ATM classifier proposed by Otero, Freitas & Johnson (2012) builds decision trees using ant colony optimization instead of traditional C4.5 or CART techniques. Our experimental process ensures reliability, comparability and reproducibility, which are lacking in some previous research within the field. This approach is intended to improve on previous studies combining both domains. The ATM classifier has not been tested in the intrusion detection domain.en_US
dc.language.isoenen_US
dc.publisherAcademic Conferences and Publishing International Limiteden_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/za/-
dc.subjectAnt Tree Miner (ATM)en_US
dc.subjectAnt Colony Optimisation (ACO)en_US
dc.subjectDecision Treesen_US
dc.subjectIntrusion Detection (ID)en_US
dc.subjectSwarm intelligenceen_US
dc.titleAnt colony induced decision trees for intrusion detectionen_US
dc.type.patentOtheren_US
dc.relation.conferenceProceedings of the 16th European Conference on Cyber Warfare and Security (ECCWS 2017), Dublin, Ireland, 29 - 30 June 2017en_US
Appears in Collections:Appsc - Conference Proceedings
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