Network Intrusion Detection using Neural Network Based Classifiers
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2018 by IJCTT Journal|
|Year of Publication : 2018|
|Authors : Ashalata Panigrahi, Manas Ranjan Patra|
|DOI : 10.14445/22312803/IJCTT-V59P121|
Ashalata Panigrahi, Manas Ranjan Patra "Network Intrusion Detection using Neural Network Based Classifiers". International Journal of Computer Trends and Technology (IJCTT) V59(3):121-125, May 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Rapid expansion of computer networks throughout the world has made data security a major concern. In the recent past, there have been incidences of cyber-attacks which have put data at risk. Therefore, developing effective techniques to secure valuable data from such attacks is the need of the hour. Several intrusion detection techniques have been developed to deal with network attacks and raise alerts in a timely manner in order to mitigate the impact of such attacks. Among others, ANN methods can provide multilevel, multivariable security system to meet organizational needs. In this work, we have applied four prominent neural network based classification techniques, viz., Self-Organizing Map, Projective Adaptive Resonance Theory, Radial Basis Function Network, and Sequential Minimal Optimization to predict possible intrusive behavior of network users. The performance of these techniques have been evaluated in terms of accuracy, precision, recall / detection rate, F-Measure, and false alarm rate on the standard NSL-KDD intrusion dataset.
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Intrusion detection, ANN, Classification, SOM, PART, RBFN, SMO, Ant Search, Random Search