Improved Genetically Optimized Neural Network Algorithm for Classification of Distributed Denial of Service Attack

Authors

  • Emmanuel Hamman Gadzama Federal University of Technology, Minna, Nigeria
  • Olawale Surajudeen Adebayo Federal University of Technology, Minna, Nigeria

Keywords:

DDoS, genetic algorithm, neural network, naïve bayes, machine learning

Abstract

This paper proposes a classification of distributed denial of service (DDOS) attack using neural network-based genetic algorithm (NNGA). The genetic algorithm was used to optimize neural network for the detection of DDoS attacks in order to improve the effectiveness and efficiency of classification accuracy and performance. In order to improve the NNGA, a fitness function was introduced in genetic algorithm that improved the performance of NNGA. The features of DDOS attacks from KDD 99 intrusion detection datasets were obtained to train the NNGA. The results show the improved genetically optimized neural network algorithm has better accuracy and lower false positive rate in comparison with the conventional neural network.

Author Biographies

Emmanuel Hamman Gadzama, Federal University of Technology, Minna, Nigeria

Department of Cyber Security Science, Science

Olawale Surajudeen Adebayo , Federal University of Technology, Minna, Nigeria

Department of Cyber Security

Downloads

Published

2020-09-30

How to Cite

Gadzama, E. H., & Adebayo , O. S. . (2020). Improved Genetically Optimized Neural Network Algorithm for Classification of Distributed Denial of Service Attack. LAUTECH JOURNAL OF COMPUTING AND INFORMATICS , 1(1), 58-75. Retrieved from http://laujci.lautech.edu.ng/index.php/laujci/article/view/23

Issue

Vol. 1 No. 1 (2020): Vol. 1 No. 1

Section

Articles