Enhanced intrusion detection system via agent clustering and classification based on outlier detection

Abstract

The rapid evolution of cloud computing technology has enabled seamless connection of devices on a broad spectrum. Also, it enables storage of massive quantity of data in the form of data centers. This suggests a shared pool of resources where users situated all over the world have access to the aforementioned data centers. Such a framework has cyber-security based challenges where it becomes vulnerable to external attacks. There arises a need for an Intrusion Detection System (IDS) to prevent the system from unwanted and malicious attacks. However, the existing IDS have not been able to efficiently combinehigh accuracy with low complexity and time efficiency. In order to overcome these challenges an Enhanced Intrusion Detection System via Agent Clustering and Classification based on Outlier Detection (EIDS-ACC-OD) is proposed. At first, preprocessing is performed to remove unwanted spaces using outlier detection. Then modified K-means clustering algorithm is developed for data segmentation. Further, K-Nearest Neighbor (KNN) is utilized for categorization of the attacks.