Open AccessAn Efficient Ids Based on Fuzzy Firefly Optimization and Fast Learning Network
Author(s) -
Bh Dasaradha Ram,
B. V. Subba Rao
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.36.24137
Subject(s) - artificial intelligence , false positive paradox , computer science , machine learning , firefly algorithm , classifier (uml) , firefly protocol , fuzzy logic , zoology , particle swarm optimization , biology
Overseen Interruption Recognition Framework is a framework that has the capacity of picking up from cases about past attacks to perceive new strikes. Using ANN based interruption discovery is promising for decreasing the amount of false negative or false positives in light of the fact that ANN has the capacity of picking up from certified cases. In this article, a made learning model for Quick Learning System (FLN) in light of fluffy firefly streamlining (FFO) has been proposed and named as FF-FLN. The model has been associated with the issue of interruption location and endorsed in perspective of the famous dataset KDD99. Our created strategy has been taken a gander at against a broad assortment of meta-heuristic figurings for planning ELM, and FLN classifier. FF-FLN has defeated other learning approaches in the testing exactness of the learning.