# Software-Defined Networking: Categories, Analysis, and Future Directions
## 4 Network Security and Management
The protocol dialect carries the objectives to provide robustness against downgrade attacks and specializes the network protocol in the context of network security. The OpenFlow protocol dialects have derivatives of MAC-based authentication and complete production packet security without message modifications. In [133], the optimal packet forwarding decisions in fog computing or the optical network need to be reconsidered periodically for efficient network management. Trust and security parameters in fog computing need to be reconsidered in future networks. A malicious fog node can have forged links with other fog nodes and suspicious activities in production packets. It is possible that this sort of node can lock the resources of connected services or alter the topology view at the central SDN controller. Therefore, the resultant computation and energy consumption ratio of fog computing infrastructure also increases. Similarly, in [134], the authors revealed that the current SDN standardization, especially in the form of OpenFlow, needs to be upgraded for flawless integration in fog resources. Fog computing represents a geographical distribution of resources and host applications, which makes the network more vulnerable. To tackle this vulnerability of the fog paradigm, the distributed architecture of the SDN needs to be more defensive. In [135], vehicular ad hoc networks' (VANETs) integration with SDN is analyzed. This integration supports efficient resource management for the computational offloading of moving vehicles. Likewise, this is an edge for the various network security issues. VANETs must confront such attacks, such as man-in-the-middle, DDOS, and jamming. If the SDN layer is vulnerable, then the SDN-enabled VANETs are more complex and have poorer defense mechanisms. Moving vehicle applications always trust the nearest roadside unit for computational offloading. In the case of information fabrication or privacy leaks, end-user trust declines for the infrastructure. If the central single control functions are under various attack conditions, then the SDN forwarding devices in VANETs cannot defend and classify the malicious activities or malicious hosts [136]. In IoT-enabled healthcare infrastructures, device authentication is important. The traditional network devices are deficient for authenticating or bootstrapping the fresh connected end devices securely. In [137], the objectives of SDN-supported authentication, routing from the end device to edge server, and inter-edge servers' communication (routing for the load balance) is analyzed. The proposed framework has an IoT device authentication method that is supported by the probabilistic k-nearest neighbor. The framework uses the probabilistic k-nearest neighbor to evaluate the validity of end IoT devices residing in the healthcare systems. Using p-KNN, an edge server investigates the legitimacy of healthcare IoT devices, and SDN performs efficient collaboration among the edge servers that are close to the computation resources. To localize the DDOS, a convolution neural network (CNN) is used in [138]. The study claims that the CNN can perform better for DDOS detection compared to logistic regression, multi-layered perceptron, and dense multi-layered perceptron. Furthermore, this work uses game theory to drop malicious activities. As a result, it saves the central SDN controller deployed in any ISP from the IoT devices that are intended for DDOS. If the IoT devices are impairing the central controller, then it increases the vulnerability degree of the SDN-managed ISP. This proposed approach in [139] deals with the SDN security issues to overcome DDOS in a controller and communication switch. It integrates the online learning method to limit the packet-in rate, while tending to the controller queue and switch space capacity. Traditionally, there are straight forwarding approaches to limit the packets rate, but these cannot be trustworthy for bandwidth-sensitive applications in a real network.