Multi-classifier and meta-heuristic based cache pollution attacks and interest flooding attacks detection and mitigation model for named data networking

This study's main goal is to provide a novel assault detection model with two phases. The features such node, distribution, pattern, frequency, and run time are extracted in the first phase. The Deep Convolutional Neural Network (CNN) is then used to detect cache pollution attacks (also known as content poisoning attacks), which include interest flooding for existing data, interest flooding for non-existing data, hijacking incoming interest packets, and signing data with the incorrect key. Similar to the first phase, the second phase involves extracting the aforementioned attributes and feeding them into a fuzzy decision tree (FDT) in order to identify an interest flooding attack. For accurate attack detection, this paper aims to optimise both DCNN and FDT classifiers. This work provided a fresh Decision Oriented Rider Optimisation Algorithm (DO-ROA), an enhancement of the traditional ROA, to address the optimisation problem. With regard to specific Type I and Type II performance measures, the suggested DO-ROA algorithm's performance is compared to that of other traditional models, demonstrating the superiority of the presented work.