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A Novel Immune Inspaired Concept with Neural Network for Intrusion Detection in Cybersecurity
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 12 - Number 30 |
| Year of Publication: 2020 |
| Authors: Adeniji Oluwashola David, Ukame James Joseph |
10.5120/ijais2020451863
|
Adeniji Oluwashola David, Ukame James Joseph . A Novel Immune Inspaired Concept with Neural Network for Intrusion Detection in Cybersecurity. International Journal of Applied Information Systems. 12, 30 ( June 2020), 13-17. DOI=10.5120/ijais2020451863
Abstract
Artificial immune system (AIS) that depicts the way the human immune system (HIS) responds to threats or attacks in the body . AIS was used by researchers to solve intrusion problems.Immune system algorithms like the clonal selection theory, immune networks, negative selection algorithms and danger theory concepts, although has achieved some level of results, but not adequate especially in the cybersecurity domain. In this study a model based on AIS concepts that will find a significant application in cybersecurity was developed.The negative selection algorithm (NSA) which is a class of very flexible algorithm will divide the problem space into self and non-self which was used to build the model. The detector generation phase of the NSA was improved and a neural network technique was incorporated to build the model. The developed model called NNET NSA (Neural Network Negative Selection Algorithm) used the NSLKDDCup1999 dataset to test the model. An R script was written using the R programming language and implementation was done on both Rstudio and Rapid Miner environments.Experimental results showed that the model NNET NSA achieved a high classification accuracy of 90.1% within a computation time of 15seconds as compared with two classification algorithms; support vector machine (SVM) and Naïve Bayes which achieved a classification accuracy of 65.01% and 81.66% within a computation time of both 215.81seconds and 100.15seconds respectively on the R console. The developed model (NNET NSA) further showed a low wrong classification of 3.9% as compared with SVM; 4.8% and Naive Bayes; 4.2% respectively.
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