far from the observed labels. An appropriate set of functions is the set of $l_p$-norms defined as: $$||f(x)-y||_p := \Big(\sum_{i=1}^{n}|f(x_i)-y_i|^p\Big)^\frac{1}{p},$$ where $x \in X$ is the input value, $y \in Y$ is the true label and $f(x)$ is the value predicted by the model $f$. The most commonly used norms are the $l_1$-norm and the $l_2$-norm, also known as the Manhattan norm and the Euclidean norm, respectively. A well-known example here is linear regression, in which the model class $F$ is the set of all linear functions and the standard method is the least squares method that minimizes the $l_2$-norm.

In classification learning, the labels form a finite set of values. For our purposes, we consider a binary set of labels $Y=\{0,1\}$ where $``0"$ indicates a benign network traffic and $``1"$ indicates a malicious network traffic. The most natural loss function in this setting is a function that takes the value $``1"$ if the predicted label matches the real label and $``0"$ otherwise. Due to its non-convexity, this function is sometimes replaced by the hinge loss function or the logistic loss function.

Unsupervised Learning
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In unsupervised learning, a typical data set consists only of features without labels, that is,\
