INTEREST FLOODING ATTACKS IN NAMED DATA NETWORKING

Abstract

Named Data Networking (NDN) is a newly proposed Future Internet architecture(FIA). The NDN architecture adopts name-based routing and location-independent data retrieval. Among other important features, NDN integrates security mechanisms and focuses on protecting the content rather than the communications channels. Along with a new architecture come new threats and NDN is no exception. NDN is a potential target for new network attacks such as Interest Flooding Attacks (IFAs). Attackers take advantage of IFA to launch (D)DoS attacks in NDN. In this dissertation, we focus on the Interest Flooding Attack and present our contributions to deal with this threat. Our first contribution is a novel congestion-aware solution that differentiates between intentional and unintentional misbehavior due to the network congestion when detecting Interest Flooding Attack. We prove, through simulation evaluation, the efficiency of our solution in congested network scenarios. Our second contribution, named MSIDN, is a cooperative solution that detects and mitigates sophisticated versions of IFA. The proposed solution is capable of stopping the malicious traffic without affecting legitimate users. Through extensive simulation evaluation, we show the efficiency of our solution in different ttacking scenarios. Our third contribution is a series of unconsidered adversarial models. We present a multitude of non-conventional IFA scenarios that the actual solutions did not take into consideration. we finish our work by eviewing the requirements for a complete and efficient IFA solution and pinpoint the various issues encountered by IFA detection and mitigation mechanisms before shedding the light on the future research directions regarding IFAs.