Machine learning-based centralized link coding attack detection in software-defined network

High-level network management is achieved by using a software-defined network (SDN). The SDN clearly differentiates the data plane using effective programs. The centralized controllers in the SDN have much vulnerability to various types of attacks by fake hosts. One of the ways to attack is link coding. This link coding attack easily disconnects the data plane from the control plane by creating more traffic in the network. This makes SDN poor centralized manageability. In this paper, we demonstrate the vulnerability of the SDN control layer to link coding and how the attack strategy differs when targeting traditional networks which primarily involves attacking the links directly. In link coding, the attacker employs bots to surreptitiously send low-rate legitimate traffic on the control channel which ultimately results in disconnecting the control plane from the data plane. To address this challenge, a machine learning-based classifier is used to alleviate link coding in SDN. This paper proposed a Random Forest-based Adaptive Gradient Algorithm (RF-AGA). The gradient descent optimization method uses standard gradient as the objective function to search the target but adaptive gradient uses adaptive sizes for each input data in objective functions. Here deep learning techniques are used to classify the network traffics and is implemented as an extension module in the Floodlight controller. The accuracy rate of Naive Bayesian was 81.11%, J-48 87.54%, Random Forest 91.02%, and the proposed work got 93.15%.