Intrusion Detection Using Back Propagation Neural Network and Quick Reduct Algorithms

Authors

  • S. Vijaya Rani  Research Scholar, Bharathiar University, Tamil Nadu, India
  • Dr. G. N. K Suresh Babu  Associate Professor, Department of MCA, Acharya Institute of Technology, Bangalore, Karnataka, India

DOI:

https://doi.org//10.32628/CSEIT183891

Keywords:

IDS, Normalization, Neural Network, Threats, BPN, QR, ED

Abstract

It is a big challenge to safeguard a network and data due to various network threats and attacks in a network system. Intrusion detection system is an effective technique to negotiate the issues of network security by utilizing various network classifiers. It detects malicious attacks. The data sets available in the study of intrusion detection system were DARPA, KDD 1999 cup, NSL_KDD, DEFCON, ISCX-UNB, KDD 1999 cup data set is the best and old data set for research purpose on intrusion detection. The data is preprocessed, normalized and trained by BPN algorithm. Further the normalized data is discretized using Entropy discretization and feature selection carried out by quick reduct methods. After feature selection, the concerned feature from normalized data is processed through BPN for better accuracy and efficiency of the system.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-12-30

Issue

Volume 3, Issue 8, November-December-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
S. Vijaya Rani, Dr. G. N. K Suresh Babu, " Intrusion Detection Using Back Propagation Neural Network and Quick Reduct Algorithms, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 8, pp.317-325, November-December-2018. Available at doi : https://doi.org/10.32628/CSEIT183891