MODIFIED BEE COLONY WITH BACTERIAL FORAGING OPTIMIZATION BASED HYBRID FEATURE SELECTION TECHNIQUE FOR INTRUSION DETECTION SYSTEM CLASSIFIER MODEL

Abstract
Feature selection (FS) plays an essential role in creating machine learning models. The unrelated characteristics of the data disturb the precision of the perfection and upsurges the training time required to build the model. FS is a significant process in creating the Intrusion Detection System (IDS). In this document, we propose a technique for selecting container functions for IDS. To develop the performance capacity of the modified Artificial Bee Colony (ABC) procedure, a hybrid method is presented in which the swarm behavior of the Bacterial Foraging Optimization (BFO) algorithm is entered into the Modified Bee Colony (MBC) procedure to perform a local search. The proposed Hybrid MBC-BFO algorithm is analyzed with three different classification techniques which are separately analyzed to verify the proposed performance. The classification techniques are Artificial Neural Networks (ANN), Recursive Neural Network (ReNN), and Recurrent Neural Network (RNNs). The proposed algorithm has passed several algorithms for selecting advanced functions in terms of detection accuracy, recall, precision, false positive rate, and F-score.

Authors
S Kalaivani, G Gopinath
Bharathidasan University, India

Keywords
Swarm Intelligence, Modified Bee Colony, Bacterial Foraging Optimization, Feature Selection, IDS, KDDCUP’99
Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 10 , Issue: 4 )
Date of Publication :
July 2020
DOI :

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