Scalable Improved Quick Reduct: Sample Based

2012 ◽  
pp. 34-39
Author(s):  
P. S. V. S. Sai Prasad ◽  
C. Raghavendra Rao
Keyword(s):  
Quick Reduct

Scalable Quick Reduct Algorithm

2016 ◽  
Author(s):  
Praveen Kumar Singh ◽  
P. S. V. S. Sai Prasad
Keyword(s):  
Quick Reduct

Tolerance rough set firefly-based quick reduct

2016 ◽  
Vol 28 (10) ◽  
pp. 2995-3008 ◽  
Author(s):  
Jothi Ganesan ◽  
Hannah H. Inbarani ◽  
Ahmad Taher Azar ◽  
Kemal Polat
Keyword(s):  
Rough Set ◽  
Tolerance Rough Set ◽  
Quick Reduct

Intrusion Detection Using Back Propagation Neural Network and Quick Reduct Algorithms

2018 ◽  
pp. 317-325
Author(s):  
S. Vijaya Rani ◽  
G. N. K Suresh Babu
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Effective Technique ◽  
Data Set ◽  
Quick Reduct

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.

Sign in / Sign up

Export Citation Format

Share Document