An Intelligent Gray Wolf Optimizer: A Nature Inspired Technique in Intrusion Detection System (IDS)

Abstract

Abstract

Feature choice algorithmic program investigates the knowledge to get rid of creaking, irrelevant, overabundance information, and everyone the while optimizes the classification performance. In this work, an intelligent grey wolf optimization (GWO) technique is used to select optimal feature subset for classification purposes. Grey wolf optimizer (GWO) is one of the most recent bio-enlivened optimization strategies, which impersonate the authority pecking order and chasing system of gray wolves in nature. The point of the intelligent gray wolf optimization is to look ideal regions of the intricate pursuit space through the correspondence of individuals in the masses. In this manner, feature selection is considered to be used in pre-handling before applying classifier to an informational index. Accordingly, this great decision of feature selection prompts the high grouping precision and limits computational expense. KDD CUP 1999 data-sets are used to experiment for Intrusion Detection System (IDS). The experimental result shows comparison of accuracy, sensitivity and specificity of IDS data-set using different classification techniques. The main aim is to select minimum number of features which will be optimal and giving high grouping precision of expansive datasets to make PC framework intrusion free.

Keywords: Grey wolf optimizer (GWO), IDS, KDD CUP1999, SVM, KNN, GRNN

Cite this Article

Durgesh Srivastava, Rajeshwar Singh, Vikram Singh. An Intelligent Gray Wolf Optimizer: A Nature Inspired Technique in Intrusion Detection System (IDS). Journal of Advancements in Robotics. 2019; 6(1): 18–24p.