Author:Mzila, P; Dube, EDate:Jul 2013As internet usage rapidly increases in both private and corporate sectors, the study of network intrusion detection is continuously becoming more relevant and has thus been evolving substantially in recent years. One of the most interesting ...Read more
Author:Naidoo, T; Tapamo, JR; McDonald, Andre MDate:Sep 2015A feature selection algorithm that is novel in the context of anomaly–based network intrusion detection is proposed in this paper. The distinguishing factor of the proposed feature selection algorithm is its complete lack of dependency on ...Read more
Author:Naidoo, Tyrone; McDonald, Andre M; Tapamo, J-RDate:Sep 2015A feature selection algorithm that is novel in the context of anomaly–based network intrusion detection is proposed in this paper. The distinguishing factor of the proposed feature selection algorithm is its complete lack of dependency on ...Read more
Author:Sefara, Tshephisho J; Mokgonyane, TBDate:Aug 2021Gender identification is the task of identifying the gender of the speaker from the audio signal. Most gender identification systems are developed using datasets belonging to well-resourced languages. There has been little focus on creating ...Read more
Author:Moepya, Stephen O; Nelwamondo, Fulufhelo V; Twala, BDate:Apr 2017Financial statement fraud has proven to be difficult to detect without the assistance of data analytical procedures. In the fraud detection domain, minority class instances cannot be readily found using standard machine learning algorithms. ...Read more
Author:Ajoodha, R; Klein, R; Rosman, Benjamin SDate:Nov 2015In this paper we use content-based features to perform automatic classification of music pieces into genres. We categorise these features into four groups: features extracted from the Fourier transform’s magnitude spectrum, features designed ...Read more