Title :
Classification model of network intrusion using Weighted Extreme Learning Machine
Author :
Srimuang, Worachai ; Intarasothonchun, Silada
Author_Institution :
Dept. of Comput. Sci., Khon Kaen Univ., Khon Kaen, Thailand
Abstract :
The development of a model classification intrusion detection using Weighted Extreme Learning Machine was examined with KDD´99 data set ad 4 types of main attack : Denial of Service Attack (DoS), User to Root Attack (U2R), Remote to Local Attack (R2L), and Probing Attack, when comparing the effectiveness of working process of the method presented to SVM+GA[6] and ELM, found that weighted technique using RBF Kernel activation function which the value of trade-off constant C was at 25, which was presented the average effectiveness of accuracy to be more effective than other 2 techniques, giving accuracy effectiveness value of DoS = 99.95%, U2R = 99.97%, R2L = 93.64% and Probing = 96.64 %, meanwhile it used less time for working. This could be an interesting technique to be applied to enhance the effectiveness of security of system surveillances in monitoring to be able to remedy the situations on time.
Keywords :
computer network security; learning (artificial intelligence); pattern classification; radial basis function networks; support vector machines; DoS; ELM; KDD´99 data set; R2L; RBF kernel activation function; SVM+GA; U2R; denial of service attack; network intrusion classification model; probing attack; remote to local attack; system surveillance security; user to root attack; weighted extreme learning machine; Accuracy; Computer crime; Data models; Intrusion detection; Kernel; Training data; Imbalance; Intrusion Detection System; Trade-off constant C; Weighted Extremes Learning Machine;
Conference_Titel :
Computer Science and Software Engineering (JCSSE), 2015 12th International Joint Conference on
DOI :
10.1109/JCSSE.2015.7219794
