## 4 Results
### ML and DL Algorithms for Cybersecurity
#### 4.2.5 Convolutional Neural Networks  
Compared to other deep learning algorithms, CNNs can learn from raw data. As a result, data extraction, which is generally done before training a model, is no longer necessary. Hidden networks, pooling networks, convolutional networks, and fully-connected networks are frequently included in convolutional networks (CNNs). In terms of cyber security, CNN lacks a particular leader in the field. The many security and privacy issues that organizations currently confront have led to the development of many CNN-based methods. For instance, as part of a cutting-edge method for identifying abnormal incursions, CNN was used to create a multiclass classification model for IoT networks. This process was used to find any possible threats [115]. The authors in [116] used this technique to find cyber-attacks on industrial control systems to create a small version of a wide range of industrial water treatment facilities. In [117], To recognize DoS attacks on IoT networks, writers used CNN. A distinct deep CNN technique was also recommended for malware identification [118]. It also allows the network to be used successfully on a GPU. A multi-CNN fusion technique was suggested to detect intrusion attacks on industrial IoT networks [119]. Thousands of sensors are being included in the smart grid's infrastructure to make the switch from a traditional grid to a smart grid. These sensors produce enormous amounts of data in the form of log files or time series data since they continuously check the health of the hardware to which they are connected. A smart grid system has several different kinds of sensors, including those that measure voltage, current, module temperatures, and irradiance. The information gathered by these sensors is processed before being sent to a server for storage. Both local and remote hosting options exist. The most secure way to store data is on a local server, but doing so limits the data's usefulness for identifying novel patterns or developing a deeper understanding of the subject of the study. The user has more control over how data is used when information is stored on a cloud server, accessible from a distance, and scraped to a computer using the GETS command. Machine learning approaches have lately been effective at locating cases of cyber intrusion. By examining past events, machine learning, on the other hand, may be able to identify intrusions. Toprepare for power outages, 54 linked J Ripper with Ada boost. The model divided the data into three categories based on its findings (assault, natural disturbances, and no event). An attack known as the false data injection attack is typical and has the potential to damage smart grid networks seriously. For utilities and consumers, tampering with data from smart meters might be pretty expensive. To locate the FDIA, researchers used ensemble-based machine learning [55]. Figure 8 shows the supervised learning process. The IEEE 14 bus system was used to evaluate the model. Unlike linear regression, naive Bayes, decision trees, and support vector machines, unsupervised ensemble models were more accurate than individual models, with the most incredible accuracy reaching 73%. The multilayer perceptron is used in [56] to examine how FDIA affects AI-based smart grids thoroughly. The study found that even if only 20% of the data is false, machine learning algorithms' accuracy might drop by 15%. This may significantly impact the decision-making process for the smart grid. Suppose a disruption happens, and the model cannot predict it because of inaccurate data, for example. In that case, data poisoning could cause the grid to become unstable and have unfavorable effects. The entire system can be negatively affected. The authors in [33] suggests using a conditional deep belief network technique to identify FDIA for power theft in real-time. The IEEE 118 bus and the IEEE 300 bus were used to test the model. The model's outcomes contrasted with support vector machines and artificial neural networks.