Big Data Analytics for Cyber Security using binary crow search algorithm based Deep Neural Network

Cyber security is the one of the big challenges in Big Data Analytics. The prediction of cyber attackers or threats is performed by presenting machine learning algorithms and Deep Learning algorithms. However, accuracy of the prediction model is further to be improved so that an enhanced DNN, a Deep...

Full description

Saved in:

Bibliographic Details
Published in:	2022 IEEE 7th International conference for Convergence in Technology (I2CT) pp. 1 - 5
Main Authors:	Banoth, Rajkumar, Godishala, Aruna Kranthi
Format:	Conference Proceeding
Language:	English
Published:	IEEE 07.04.2022
Subjects:	Big Data Binary Crow Search Algorithm (BCSA) Cyber security Deep learning deep neural network (DNN) Feature extraction Machine learning algorithms Neural networks Prediction algorithms Predictive models
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Cyber security is the one of the big challenges in Big Data Analytics. The prediction of cyber attackers or threats is performed by presenting machine learning algorithms and Deep Learning algorithms. However, accuracy of the prediction model is further to be improved so that an enhanced DNN, a Deep Neural Network is offered here in this paper and this enhanced DNN is utilized as a prediction model. The performance of the DNN is improved with the Binary Crow Search Algorithm (BCSA). Using this algorithm, weight parameters of DNN are optimized. Simulation outcomes illustrate that the projected prediction model outpaces the existing DNN in terms of Recall, Precision, F-score and Accuracy.
DOI:	10.1109/I2CT54291.2022.9824868