Title :
Comparison of genetic algorithm optimization on artificial neural network and support vector machine in intrusion detection system
Author :
Dastanpour, Amin ; Ibrahim, Suhaimi ; Mashinchi, Reza ; Selamat, Ali
Author_Institution :
Adv. Inf. Sch., Univ. Teknol. Malaysia, Kuala lumpur, Malaysia
Abstract :
As the technology trend in the recent years uses the systems with network bases, it is crucial to detect them from threats. In this study, the following methods are applied for detecting the network attacks: support vector machine (SVM) classifier, artificial Neural Networks (ANN), and Genetic Algorithms (GA). The objective of this study is to compare the outcomes of GA with SVM and GA with ANN and then comparing the outcomes of GA with SVM and GA with ANN and other algorithms. Knowledge Discovery and Data Mining (KDD CPU99) data set has been used in this paper for obtaining the results.
Keywords :
data mining; genetic algorithms; neural nets; security of data; support vector machines; ANN; SVM classifier; artificial neural network; data mining; genetic algorithm optimization; intrusion detection system; knowledge discovery; support vector machine; Artificial neural networks; Classification algorithms; Feature extraction; Genetic algorithms; Intrusion detection; Machine learning algorithms; Support vector machines; Artificial Neural Network (ANN); Genetic algorithm (GA); Support Vector Machine (SVM); intrusion detection; machine learning;
Conference_Titel :
Open Systems (ICOS), 2014 IEEE Conference on
Conference_Location :
Subang
Print_ISBN :
978-1-4799-6366-9
DOI :
10.1109/ICOS.2014.7042412
