# Detecting Cryptojacking Web Threats: An Approach with Autoencoders and Deep Dense Neural Networks
## 6 Results and Discussion
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Authors** & **Algorithm** & **Hyper-Parameters (\(\lambda\))** & **Values** \\ \hline [PERSON] et al. [31] & Fuzzy C-Means & fuzzifier (\(m\)): Controls how fuzzy the inter-group boundaries will be & \(\{0.5,5\}\) \\ \hline [PERSON] et al. [38] & SVM & Penalty parameter \(C\) of error term & \(\{10,100\}\) \\ and [PERSON] et al. [44] & SVM & Type of division (\(div\)) & One-vs-one, One-vs-rest \\  & SVM & Kernel type (\(ker\)) & Polynomial, Linear, RBF \\ \hline  & MLP & Hidden layer sizes (\(H_{1}\in\mathbb{R}^{n}\)) & \(32,64,128\) \\ [PERSON] et al. [38] & MLP & Activation Functions (\(act\)) & Sigmoid and hyperbolic tangent \\  & MLP & Weight optimization solver (\(sol\)) & ADAM and Stochastic Gradient Descent \\ \hline  & RF & Attribute selection method (\(asm\)) & GNN Index and Entropy \\  & RF & Max tree depth (\(mtd\)) & \(\{1,15\}\) \\ [PERSON] et al. [38], & RF & Number of features to consider for best split (\(nfBS\)) & \(\{2,18\}\) \\ [PERSON] et al. [43] and & RF & Minimum number of samples required to be at leaf node (\(mmsLN\)) & \(\{1,30\}\) \\ [PERSON] et al. [12] & RF & Minimum number of samples required to split internal nodes & \(\{1,30\}\) \\  & RF & (\(mmsIN\)) & \(\{1,30\}\) \\  & RF & Minimum number of trees in forest (\(mmTF\)) & \(\{1,5\}\) \\ \hline  & MISVM & Penalty parameter \(C\) of error term & \(\{1,10\}\) \\  & MISVM & Kernel type (\(ker\)) & Polynomial, Linear, RBF \\  & MISVM & Number of bags (\(nb\)) & \(10,20,30,40,50\), with 100 \\ [PERSON] et al. [48] & RS + DT & Number of estimators (trees) & samples per class each \\  & RS + DT & Maximum number of features in each estimator (mnFE) & \(2,10\) \\  & SMO + SVM & Penalty parameter \(C\) of an error term & \(\{100,1000\}\) \\  & SMO + SVM & Kernel type (ker) & Polynomial, Linear, RBF \\ \hline  & LR & Inverse of regularization strength of term \(C\) & \(\{0.1,1\}\) \\ [PERSON] et al. [49] & LR & Norm selected to regularize the cost function (\(\ell_{n}\)) & \(\ell_{1}\) and \(\ell_{2}\) \\  & LR & Optimization algorithm (opt) & Linear, LBFGS, SAG, SAGA \\ \hline \hline \end{tabular}
\end{table} 
Table 8: List of selected authors who have presented solution proposals regarding cryptojacking, using shallow algorithms. The \(\lambda\) hyper-parameters to be evaluated and then optimized are described. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Authors** & **Algorithm** & **Configuration** \\ \hline [PERSON] et al. [16] & CNN & 1. 1D convolution layer \(L_{1}\in\mathbb{R}^{1\times 18}\) with 64 filters, a kernel size with 3 units and as an activation function ReLU \\  & & 2. First pooling layer with two units \\  & & 3. 1D convolution layer \(H_{1}\) with 32 filter, a kernel size with 3 units and as an activation function ReLU \\  & & 4. Second pooling layer with two units \\  & & 5. A dense hidden layer \(H_{2}\in\mathbb{R}^{32}\) with a ReLu activation function \\  & & 6. The output layer \(O_{1}\in\mathbb{R}\) with a Sigmoid activation function. \\ \hline [PERSON] et al. [47] & LSTM & 1. LSTM input layer 1. \(L_{1}\in\mathbb{R}^{1\times 18}\) with 32 units \\  & & 2. One Dropout layer with a radius of.2 \\  & & 3. LSTM hidden layer \(H_{1}\in\mathbb{R}^{1\times 18}\) with 16 units \\  & & 4. The output layer \(O_{1}\in\mathbb{R}\) with a Sigmoid activation function. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: List of authors who presented cryptojacking solution proposals, using DL algorithms. The layers and units used for the construction of the models are described. From Table 9, a general configuration was proposed comprising: the loss function via binary cross-entropy, ADAM optimization, and Accuracy as validation metrics. The overall results, with the aforementioned performance metrics, can be observed in Table 10.  
As shown in Table 10, after brute-forcing different hyper-parameters around the grid search technique, the algorithms that are closer to neural behavior, MLP [38], and RL [49], perform better on the F1 score ratio and reduced the training time.