Evaluating Machine Learning Algorithms for Detection of Interest Flooding Attack in Named Data Networking

Named Data Networking (NDN) is one of the most promising data-centric networks. NDN is resilient to most of the attacks that are possible in TCP/IP stack. Since NDN has different network architecture than TCP/IP, so it is prone to new types of attack. These attacks are Interest Flooding Attack (IFA), Cache Privacy Attack, Cache Pollution Attack, Content Poisoning Attack, etc. In this paper, we discussed the detection of IFA. First, we model the IFA on linear topology using the ndnSIM and CCNx code base. We have selected most promising feature among all considered features then we applied diinAerent machine learning techniques to detect the attack. We have shown that result of attack detection in case of simulation and implementation is almost same. We modeled IFA on DFN topology and compared the results of different machine learning approaches.