Computer Network Attack Detection Using Enhanced Clustering Technologies

Authors

  • Dhamyaa Salim Mutar Business Administration Department, Baghdad College of Economic Sciences University, Baghdad, Iraq

DOI:

https://doi.org/10.24203/ajas.v9i6.6839

Keywords:

FFNN, KDD, K-means, DB Scan, Training, Testing, Intrusion, Attack, Weights, Performance

Abstract

The need for security means has brought from the fact of privacy of data especially after the communication revolution in the recent times. The advancement of data mining and machine learning technology has paved the road for establishment an efficient attack prediction paradigm for protecting of large scaled networks. In this project, computer network intrusions had been eliminated by using smart machine learning algorithm. Referring a big dataset named as KDD computer intrusion dataset which includes large number of connections that diagnosed with several types of attacks; the model is established for predicting the type of attack by learning through this data. Feed forward neural network model is outperformed over the other proposed clustering models in attack prediction accuracy.

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Published

2021-12-31

How to Cite

Mutar, D. S. (2021). Computer Network Attack Detection Using Enhanced Clustering Technologies. Asian Journal of Applied Sciences, 9(6). https://doi.org/10.24203/ajas.v9i6.6839