## 4 Results
### Cyber-Attacks and Security Risks
The term \"smart grid\" describes a power system that makes use of sensing technologies, communication, digital control, information technology, and other field equipment to coordinate its current operations and improve the efficiency and responsiveness of the power grid. The Photovoltaic Generation System may be tracked and measured with the help of the Internet of Things, and the WSN in particular [79]. The Internet of Things has also been utilized in agriculture to find farming-friendly places so that the correct plants can be planted [80]. IoT is used in medical to track heart rate. It is feasible to build Smart door locks with Mobile Backend as a Service and home automation and smart security systems with Low Cost Real-Time [81] using an ESP 8266, a straightforward and affordable Internet of Things key. Researchers in a range of fields are interested in neuro-fuzzy systems because of their better learning and reasoning capabilities. In neuro-fuzzy systems, the representation of implicit information via fuzzy inference systems and the ability of artificial neural networks to learn from their experiences are merged. Because of the speed, accuracy, and difficulty of creating computers, researchers have thought about using soft computing techniques to characterize, forecast, and manage dynamic nonlinear systems. Fuzzy logic systems and artificial neural networks are examples of soft computing techniques. To address critical difficulties, a number of research and engineering sectors are starting to combine the two schools of thinking. An intelligent machine's ability to reason and draw conclusions can be greatly enhanced by fuzzy logic. Fuzzy logic describes qualitative yet flawed data, allowing machine learning to be symbolically expressed. Neural networks are used because they can learn, are reliable, and offer a lot of parallel to a system. The neuro-fuzzy system is a great place to start when trying to solve machine learning problems because it can represent knowledge and self-learn. The Takagi-Sugeno-Kang fuzzy inference method is the most effective way to represent nonlinear dynamic systems. As a \"multimodal\" technique, TSK system modelling can use linear sub models to show how a complex nonlinear dynamic system behaves as a whole. One of the most well-liked neuro-fuzzy methods is ANFIS. Regression, modelling, forecasting, and control have all used it. The ANFIS utilizes a fuzzy inference system of the TSK type on a 5-layer network design. The two types of parameters in ANFIS are assumption and consequence. The relationship between the two groups of variables is described using fuzzy if-then rules. The biggest problem with ANFIS is that it uses a lot of computer resources and frequently produces models that are unnecessarily complex for even the most straightforward problems. The accuracy and training time of standard neuro-fuzzy networks have recently increased due to recent developments in learning algorithms and network architecture. A neuro-fuzzy system needs the following qualities to perform well: Positive qualities include one that can learn quickly, adapt on the fly, continuously optimize itself to attain the minimum possible global error, and use the least amount of computing power possible. Because hybrid techniques are used to continuously good them, most neuro-fuzzy inference systems take a long time to learn. On occasion, it's necessary to manually change some parameters. On the other hand, overfitting and local minima are easily induced by diffusion learning methods. While the input weights and hidden layer biases are chosen at random and can be thought of as a linear system, the output weights of ELM are determined using a straightforward generalized inverse operation. as opposed to the norm. Most CPSGs rely on wireless communication, it is simple for enemies to target that channel. Information technology attacks are those that limit access to data. Classical intricate attacks operate on communication networks such as cognitive radio networks and mobile Adhoc networks [82].