A Charging/Rewarding mechanism-based Interest Flooding Attack mitigation strategy in NDN

Named Data Networking (NDN) is one of the next generation network architectures, the consumer requests the data by sending an Interest packet and the network responds to each request uniquely, effectively avoiding many Distributed Denial of Service (DDoS) attacks under the traditional architecture. However, there is a new type of attack in NDN - Interest Flooding Attack (IFA), in which malicious consumers send a lot of requests for non-existent data, causing the network to fail to serve legitimate consumers. How to accurately identify malicious consumers to fight IFA becomes an urgent problem to be solved. Therefore, this paper proposes a Charging/Rewarding mechanism based on Hidden Markov Model (HMM) to fight IFA. This method is based on the external characteristic parameters of the consumers to establish HMM and then identify malicious consumer. The Hidden Markov Model is established by the Baum-Welch algorithm. The forward algorithm is used to compute the probability of the observed external characteristic parameters sequence appears in the given model, and the Viterbi algorithm is used to predict the state of the consumer. In order to limit the malicious consumer and stimulate the legitimate consumer, the edge router will charge consumers when providing forward service and reward legitimate consumers. Finally, the effectiveness of the countermeasure is verified through experiment. The experimental results show that the countermeasure can increase the consumer Interest packet satisfaction ratio by 85.9% and 88.6% at tree and net-like topology respectively.