IDS: Policy-based Security Using Software-Defined Networking System

Abstract

Dismissed and unrelated features in data have caused a long-term problematic in network traffic
classification. These geographies not only slow down the procedure of organization but also prevent a
classifier from making precise choices, especially when coping with big data. In this paper, we propose a
shared information based algorithm that logically selects the optimum feature for classification. This shared
information based feature selection algorithm can handle linearly and nonlinearly dependent data features.
Its effectiveness is evaluated in the cases of network intrusion detection. An Interruption Finding Scheme,
named Least Honest Support Vector Machine based IDS (LSSVM-IDS), is built using the constructions
selected by our proposed article selection algorithm. The presentation is estimated using three interruption
finding estimation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006 dataset. The estimation results
show that our feature selection algorithm enhances more serious features to achieve better correctness and
lower computational cost associated with the state-of-the-art methods.