Open AccessDetecting Intrusion with High Accuracy: using Hybrid K-Multi Layer Perceptron
Author(s) -
Amit Dogra*,
Taqdir
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c5645.098319
Subject(s) - intrusion detection system , computer science , cluster analysis , perceptron , margin (machine learning) , data mining , intrusion , abnormality , anomaly based intrusion detection system , layer (electronics) , artificial intelligence , mechanism (biology) , pattern recognition (psychology) , machine learning , artificial neural network , psychology , social psychology , philosophy , chemistry , geochemistry , organic chemistry , epistemology , geology
The intrusion detection is the mechanism by which abnormality from the state driven dataset is discovered. The intrusion causes the problem of false discovery that mislead overall result. The resources from server may not be accessed by the use of intrusion be malicious users. The propose mechanism of Self organizing KMLP technique to discover abnormal patterns from the dataset. The dataset is synthetically derived to demonstrate the experimental work. The operation is demonstrated against K-Map clustering. The result is presented in terms of classification accuracy, number of attacks and execution time and result shows significant improvement by the margin of 10%.