# Smart Attacks Learning Machine Advisor System for Protecting Smart Cities from Smart Threats
## 4 Classification  
Classification is accomplished via the use of a classifier algorithm. The critical element is categorization action that is associated with learning, and which is often referred to as classification techniques. ML is a method of representational learning that focuses on the evolution of systems that perform AI tasks such as prediction, diagnosis, and recognition, etc. The three main forms of learning are supervised, unsupervised, and semi-supervised [38]. ML algorithms have three main types: supervised, unsupervised, and semi-supervised learning. Deep learning is a promising learning scheme in ML [39]. Guided categorization, often known as supervised learning, is a kind of learning that enables the recognition of a set of likely classes in advance. Correctly categorized data examples are used as a training set in this procedure. In the following phase, supervised learning algorithms anticipate the outcome. Consequently, this is an excellent solution for when a certain target value is needed. This kind of learning also requires a set of training datasets that comprise both the input and the anticipated outcomes. The training set is then used to classify new data. SVMs, ANNs, logistic regression, NB, KNN, RF, DT, and other supervised learning methods are considered to be classification algorithms [40]. Descriptive or undirected categorization refers to unsupervised learning. The term \"unsupervised\" refers to data that does not have an expected outcome. Clustering, self-organizing map (SOM), and deep learning, are well-known unsupervised learning methods. Semi-supervised learning uses labeled and unlabeled data to combine supervised and unsupervised learning [41]. The NB, SVMs, ANNs, DT, ensemble learning, and ELM are discussed. A comparison of these algorithms is in Table 2.  
* **Naive Bayesian** The NB classifier is a primary probabilistic classifier that is often used to identify network intrusions. It combines previous knowledge with sample data and utilizes statistical deductions, which use probability to show various sorts of uncertainties. The concept that all input attributes are uncorrelated to one another forms the basis of its principles. As a result, it shows how likely it is that an object is from a specific class. From [PERSON] et al. [42], in order to use the time-NetFlow link, the NB method was paired with a time slicing function, as network traffic fluctuates at various periods and some traffic does not appear at all. * **Support Vector Machines (SVMs)** A SVM is used to look for patterns. It uses use supervised learning ideas such as kernels (e.g., radial basis function (RBF)), sparseness, the lack of local minima, and capacity checking accomplished by acting on the boundary (the hyperplane solution's distance from its nearest point). In the learning domain, classifiers with a high degree of generalization correctly predict the class of new input [43]. From [PERSON] et al. [44], the least squares support vector machine has been proposed as a basis for an IDS, a basic SVM classifier (LS-SVM) variation. Compared to a standard SVM, this modification is more prone to be influenced by outliers and noise in the training set. There are two steps to their decision-making process. The first step is responsible for shrinking the dataset to a manageable size using an optimum allocation approach that selects samples based on data variability. The LS-SVM is then fed these representative samples in the following step. This technique was designed to function with incremental and static data, which yielded good performance. * **Artificial Neural Networks (ANN)** ANNs are mathematical representations of the structure of the brain of intelligent creatures that learn via experience. In reaction to inputs and responses from the environment in which they function, they are capable of self-adaptation, self-organization, and learning. While NNs are inspired by biological design, they are mainly used as classifiers in anomaly detection.