Named data networking (NDN) corresponds to content‐centric networking, content‐based networking, and data‐oriented networking. Future internet architecture is modelled to overcome the fundamental limitations of the present internet protocol‐based internet and to provide specific strong security. Caching is a key NDN feature in the network. However, pervasive caching strengthens security issues in particular cache pollution assaults including cache poisoning (e.g. presenting malicious content in caches as false‐locality) and cache pollution (e.g. unpopular content is ruined with cache locality as locality‐disruption). In this work, a new cache replacement method based on the radial basis function neural network (RBFNN) is proposed to detect and mitigate the cache pollution attacks in NDN. RBFNN framework is constructed utilising the input associated with the cached content inherent characteristics and output data associated with the content type, i.e. locality‐disruption, false‐locality, and healthy. Experimental results show the efficiency as well as the effectiveness of the proposed method in terms of hit damage ratio and computational time.

Named Data Networking (NDN) is a developing Internet design that utilizes a new network communication model dependent on the identity of Internet content. Its core component, the Pending Interest Table (PIT) serves an important role of recording Interest packet information. In managing PIT, the issue of flow PIT measuring has been very challenging because of the huge use of long Interest lifetime especially when there is no adaptable replacement strategy, subsequently affecting PIT performance. Named Data Networking (NDN) might experience some emerging threats such as Interest Flooding Attacks (IFA). In this paper, we focus on the IFA that can seriously devour the memory resource for the Pending Interest Table (PIT) of each included NDN router by flooding a huge amount of malicious Interests with spoofed names. To extricate the pressure of PIT attacked by IFA, we propose a methodology of efficient Secured PIT management and attack detection strategy by using a cuckoo search optimization algorithmDeep convolutional neural network (CSOA-DCNN) algorithm in Named Data Network. The CSO algorithm initially utilizes a learning technique and afterward considers improved search operators and deep convolutional neural network architecture (DCNN) for classification. The network simulation tool is utilized to design and calculate PIT management. The results of the study on a 20 Gbps gateway trace shows that the corresponding PIT contains 1.5 M entries, and the lookup, insert and delete frequencies are 1.4 M/s, 0.9 M/s and 0.9 M/s. The contribution of this study is significant for Interest packet management in NDN routing and forwarding systems.

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