Friday 17th of November 2017
 

Dimension Reduction in Intrusion Detection Features Using Discriminative Machine Learning Approach


Karan Bajaj and Amit Arora

With the growing need of internet in daily life and the dependence on the world wide system of computer networks, the network security is becoming a necessary requirement of our world to secure the confidential information available on the networks. Efficient intrusion detection is needed as a defence of the network system to detect the attacks over the network. Using feature selection, we reduce the dimensions of NSL-KDD data set. By feature reduction and machine learning approach, we are able to build Intrusion detection model to find attacks on system and improve the intrusion detection using the captured data.

Keywords: Feature Selection, Weka, NSL-KDD Data Set, Accuracy.

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ABOUT THE AUTHORS

Karan Bajaj
Karan Bajaj, Btech(Computer Science & Technology) from Himachal Pradesh University and pursuing M.E(Computer Science & Engineering) from Chitkara University. He is presently working as Assistant Professor in Department of Computer Science & Engineering at Chitkara University. He has more than 3 years of teaching experience to his credit. He has attended various workshops and short-term courses in different domains.

Amit Arora
Amit Arora, ME(Computer Science & Technology) from IIT Madras. He is presently working as Assistant Professor in Department of Computer Science & Engineering at Chitkara University. His Research areas are Machine Learning, Artificial Intelligence and Data Structures and Algorithms.


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