Performance Analysis of Some Neural Network Algorithms using NSL-KDD Dataset

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-50 Number-1
Year of Publication : 2017
Authors : Jamal Hussain, Aishwarya Mishra
  10.14445/22312803/IJCTT-V50P107

MLA

Jamal Hussain, Aishwarya Mishra "Performance Analysis of Some Neural Network Algorithms using NSL-KDD Dataset". International Journal of Computer Trends and Technology (IJCTT) V50(1):43-49, August 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Consequent upon the growth of Internet and multifarious technologies including smart devices and their massive use and operations on Internet platform not only caused serious threats on security but also abnormal traffic detection. A number of assorted attacks on Internet seriously affect the systems. This not only leads to deteriorate the performance in the computer but also malfunctioning of the system. Vast growth of data in various areas due to adoption of computer technologies precipitated to anomalies. Thus, in such an alarming situation, anomalous traffic detection became a major concern of the security. Intrusion detection system is one of the redressed techniques that can be employed to determine the system security which detects the intrusion. In this paper performance of NSL-KDD dataset has been evaluated using LVQ, RBFN, DECR_RBFN, EVRBFN, MLP_BP, SONN networks of ANN showing the results that constitute binary class. Based on various performance measures analytical results were derived.

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Keywords
Neural Network, NSL-KDD, Intrusion Detection, Accuracy, LVQ, RBFN, DECR_RBFN, EVRBFN, MLP_BP, SONN.