Fusion of ANN and SVM Classifiers for Network Attack Detection
Takwa Omrani, Adel Dallali, Belgacem Chibani Rhaimi, Jaouhar Fattahi
Journal of Information Technology Review, Vol. 8, No. 4, 2017.
DOI: 
Link: http://www.dline.info/jitr/fulltext/v8n4/jitrv8n4_3.pdf
Abstract:
Attack detection is becoming one of the most active area in cybercrime detection processing. Itâ€™s importance
coincidence with the growth of network application and electronic devices (computer, mo-bile phones, android, etc). Most
of existing approaches of attack detection rely mainly on a nite set of attack. However, these solutions are vulnerable, that is,
they fail in detecting some attacks when sources of informations are ambiguous or imperfect. But, few approaches started
investigating toward this direction. Following this trends, this paper investigates the role of machine learning approach
(ANN, SVM, clustering, etc) in detecting TCP connection traffic as normal or suspicious one. But, some classifiers, using
random factors, can generate false, higher overall accuracy of detection. In this paper fusion of two classifiers has been
proposed, where artificial neural network (ANN) classi er and support vector machine (SVM) were employed. Additionally,
proposed solution allows to visualize obtained classification results. Accuracy of the pro-posed solution has been compared
with other classifier results. Experiments have been conducted with different network connection selected from NSL-KDD
DARPA dataset. Empirical results show that combining ANN and SVM techniques for attack detection is a promising direction.