DIAIF: Detection of Interest Flooding using Artificial Intelligence-based Framework in NDN android

Abstract

In today's world, information-centric networking (ICN) is a brand-new next-generation network for distributing multimedia content. ICN focuses on sharing content across the network rather than obtaining content from a single fixed server [1]. In-network caching aids in the dissemination of content from the network, and the ICN also includes a number of intrusive security mechanisms. Despite the ICN network's many security measures, several attacks, especially interest flooding attacks (IFA), continue to wreak havoc on the network's distribution capability. In order to address security threats, the literature includes a number of mitigating procedures. However, legitimate users' requests are misclassified as an attack in an emergency circumstance, affecting the network's QoS [2]. In this chapter, Detection of Interest Flooding Attack using Artificial Intelligence Framework (DIAIF) is proposed in ICN. DIAIF seeks to lighten the load on ICN routers by removing the source of the attack without interfering with legitimate user requests. DIAIF depends on router feedback to assign a beneficial value (BV) to each piece of content and to block dangerous users based on the BV. The ICN testbed was designed to assess the proposed DIAIF's performance in terms of QoS during severe flood scenarios, responding with malicious content without interfering with genuine user requests, and identifying the source of attack in a communication scenario.