Abstract
The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate mas-sive amounts of data. The traffic control and data forwarding functions are decoupled in software-defined networking (SDN) and allow the network to be programmable. Each switch in SDN keeps track of forwarding information in a flow table. The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets. Due to the obvious vast quantity of data in data centres, the capacity of the flow table restricts the data plane's forwarding capabilities. So, the SDN must handle traffic from across the whole network. The flow table depends on Ternary Content Addressable Memorable Memory (TCAM) for storing and a quick search of regulations; it is restricted in capacity owing to its elevated cost and energy consumption. Whenever the flow table is abused and overflowing, the usual regulations cannot be executed quickly. In this case, we consider low -rate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don't fit the flow table at a low rate. This study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in SDN, using Feed Forward Neural Network (FFNN), K-Means, and Decision Tree (DT). We generate two network topologies, Fat Tree and Simple Tree Topologies, with the Mininet simulator and coupled to the OpenDayLight (ODL) controller. The efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query, propagation delay, overall dropped packets, energy consumption, bandwidth usage, latency rate, and throughput. The findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G network. By putting the proposed flow method and checking whether a packet may move from point A to point B without breaking certain regulations, the evaluation tool examines every flow against a set of criteria. The FFNN with DT and K-means algorithms obtain accuracies of 96.29% and 97.51%, respectively, in the identification of collision flows, according to the experimental outcome when associated with existing methods from the literature.