A Multicriteria-Based Forwarding Strategy for Interest Flooding Mitigation on Named Data Wireless Networking

In wireless networks, the Named Data Networking (NDN) architecture maximizes contents’ availability throughout multiple network paths based on multicast-based communication combined with stateful forwarding and caching in intermediate nodes. Despite its benefits, the downside of the architecture resides in the packet flooding not efficiently prevented by current forwarding strategies and mainly associated with the native flooding of the architecture or malicious packets from Interest Flooding Attack (IFA). This work introduces iFLAT, a multicriteria-based forwarding strategy for
i
nterest
FL
ooding mitig
AT
ion on named data wireless networking. It follows a cross-layer approach that considers: (i) the Received Signal Strength (RSS) to adjust itself to the current status of the wireless network links, (ii) the network traffic (
meInfo
) to detect flooding issues and traffic anomalies, and (iii) a fake Interest Blacklist to identify IFA-related flooding. In doing so, the strategy achieves better efficiency on Interest flooding mitigation, addressing both native and IFA types of flooding. We evaluated the proposed strategy by reproducing a Flying Ad hoc Network (FANET) composed of Unmanned Aerial Vehicles (UAVs) featured by the NDN stack deployed in all nodes. Simulation results show that iFLAT can effectively detect and mitigate flooding, regardless of its origin or nature, achieving greater packet forwarding efficiency than competing strategies. In broadcast storm and IFA scenarios, it achieved 25.75% and 37.37% of traffic reduction, whereas Interest satisfaction rates of 16.36% and 51.78% higher, respectively.