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...

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Veröffentlicht in:2022 IEEE 7th International conference for Convergence in Technology (I2CT) S. 1 - 5
Hauptverfasser: Banoth, Rajkumar, Godishala, Aruna Kranthi
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 07.04.2022
Schlagworte:
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
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Zusammenfassung: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