A Novel Mobile Agent-Based Intrusion Detection Framework For Network Security Using Sl-Gat And Pp-FQCC

Abstract

Mobile Agent is known as a software component that collects the data from hosts in the network. However, security is a major problem in MA. Therefore, IDS is used to detect malicious activities in the network. But, none of the works validated whether the MA is cloned or real. Therefore, the paper presents an MA-based IDS framework for network security using SL-GAT and PP-FQCC. Firstly, the MA and host are registered with the centralized server; in the meantime, UUID is generated for the MA. Afterward, MA is securely localized by using CMP-GAO. Then, MA is authorized and the data is secured. At this point, IDS checks whether the data is attacked or not. Here, IDS is trained based on pre-processing, graph construction, and classification. Finally, the classifier classifies whether the data are attacked or non-attacked. The results proved that the proposed model achieved a high-security level of 98.87%, which outperformed prevailing techniques.