Article citationsMore>>

Hota, H.S. and Shrivas, A.K. (2014) Data Mining Approach for Developing Various Models Based on Types of Attack and Feature Selection as Intrusion Detection Systems (IDS). In: Mohapatra, D.P. and Patnaik, S., Eds., Intelligent Computing, Networking, and Informatics, Springer India, New Delhi, 845-851.
http://dx.doi.org/10.1007/978-81-322-1665-0_85

has been cited by the following article:

• TITLE: Comparison of Feature Reduction Techniques for the Binominal Classification of Network Traffic

  AUTHORS: Adel Ammar

  KEYWORDS: Intrusion Detection, Network Security, Feature Selection, Kyoto Dataset, Neural Networks, PCA, PLS

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.3 No.2, May 8, 2015

  ABSTRACT: This paper tests various scenarios of feature selection and feature reduction, with the objective of building a real-time anomaly-based intrusion detection system. These scenarios are evaluated on the realistic Kyoto 2006+ dataset. The influence of reducing the number of features on the classification performance and the execution time is measured for each scenario. The so-called HVS feature selection technique detailed in this paper reveals many advantages in terms of consistency, classification performance and execution time.