Please use this identifier to cite or link to this item: http://hdl.handle.net/11189/6263
Title: Ant tree miner amyntas: automatic, cost-based feature selection for intrusion detection
Authors: Botes, FH 
Leenen, L 
De La Harpe, R 
Keywords: Ant Tree Miner (ATM);Ant Colony Optimisation (ACO);Decision Trees (DTs);Intrusion Detection (ID)
Issue Date: 2017
Publisher: Journal of Information Warfare
Journal: Journal of Information Warfare 
Abstract: Intrusion Detection Systems (IDSs) analyse network traffic to identify suspicious patterns which indicate the intention to compromise the system. Traditional detection methods are still the norm for commercial products promoting a rigid, manual, and static detection platform. This paper focuses on recent advances in machine learning by implementing the Ant Tree Miner Amyntas (ATMa) classifier within intrusion detection. The proposed ATMa use Ant Colony Optimisation and a cost-based evaluation function to automatically select features from a data set before inducing Decision Trees (DTs) that classify network data.
URI: http://hdl.handle.net/11189/6263
ISSN: 1445-3312
Appears in Collections:FID - Journal Articles (DHET subsidised)

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