This article focusses on a rapidly evolving networking architecture known as Software Defined Networking (SDN) and the possibilities of hazards in the network. This architecture introduces decoupled infrastructure, which establishes customization in the networking system hence making it easy to manage, troubleshoot and configure. This paper focusses on the different aspects of the architecture leaving it an intermediate working in between scholarly application, adding on the elements such as security lapses, security behaviors, general security, programmability and design. In this paper, different points of weakness of the architecture have been evaluates, including the attack vector in every plane. This paper ends with a presentation for futuristic studies on the implications of attacks and potential solutions.

The term " Edge Artificial Intelligence (Edge AI)" refers to the part of a network where data is analysed and aggregated. Dispersed networks, such as those found in the Internet of Things (IoT), have enormous ramifications when it comes to "Edge AI," or "intelligence at the edge". Smartphone applications like real-time traffic data and facial recognition data, including semi-autonomous smart devices and automobiles are integrated in this class. Edge AI products include wearable health monitors, security cameras, drones, robots, smart speakers and video games. Edge AI was established due to the marriage of Artificial Intelligence with cutting Edge Computing (EC) systems. Edge Intelligence (EI) is a terminology utilized to define the model learning or the inference processes, which happen at the system edge by employing available computational resources and data from the edge nodes to the end devices under cloud computing paradigm. This paper provides a light on "Edge AI" and the elements that contribute to it. In this paper, Edge AI's motivation, definition, applications, and long-term prospects are examined.