## 2 Related Works
### Deep Learning Models  
To experimentally evaluate the hiding techniques, three representative DL architectures are considered: RNN, Bi-RNN, and MLP [32; 33; 34]. This subsection describes the architectures of these models. RNNs were initially designed to model sequential dependencies by introducing cyclic connections that propagate hidden states across time steps. Figure 3 schematically illustrates the unfolded recurrent cell used in this study. At each time step, the current input vector \(x^{(t)}\) is first projected into the hidden space via the input-to-hidden weight matrix \(U\). This input contribution is combined with the transformed previously hidden state \(h^{(t-1)}\) through the recurrent weight matrix \(W\); the result is passed through a nonlinear activation function to yield the new hidden state \(h^{(t)}\). The updated hidden state is subsequently mapped to the output \(o^{(t)}\) via the hidden-to-output weight matrix \(V\). Since the parameter triplet \(\{U,W,V\}\) is shared across all time steps, the model can capture long-range temporal dependencies while keeping the number of parameters independent of sequence length. The diagram includes the boundary states \(h^{(-1)}\) and \(h^{(+)}\), which are handled in a standard manner during implementation. The Bi-RNN architecture processes sequences in both forward and backward directions, leveraging contextual information from both past and future inputs. Figure 4 illustrates the Bi-RNN cell, which introduces a time-reversed hidden stream such that each input \(x^{(t)}\) is processed bidirectionally. The conventional forward hidden state is denoted as \(h^{(t)}\) and updated using the recurrent weight matrix \(W_{f}\), while the backward hidden state \(g^{(t)}\) propagates from future to past through its own matrix \(W_{b}\). Each direction uses its own set of input-to-hidden and recurrent weights. Specifically, the parameters for the forward hidden state \(h^{(t)}\) are \(\{U_{f},W_{f}\}\), while those for the backward hidden state \(g^{(t)}\) are \(\{U_{b},W_{b}\}\). At each step, the paired hidden states \(\{h^{(t)},g^{(t)}\}\) are concatenated and projected via a shared output weight matrix \(V\) to produce the output \(o^{(t)}\). Since \(W_{f}\) and \(W_{b}\) are reused across all time steps, the Bi-RNN achieves bidirectional context modeling with only a modest \begin{table}
\begin{tabular}{c c} \hline \hline
**Response Method** & **Method Description** \\ \hline ALU Operations & add, and, cmp, eor, mov, orr, sub \\ Shifts Operations & lsl, lsr, ror \\ Stores Operations & str, strb, strh \\ Loads Operations & ldr, ldrb, ldrh \\ Multiply Operations & mul \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Categories of assembly instructions in ELMO. increase in parameter count compared to the unidirectional RNN. For clarity, only the first instance of each weight is labeled in the figure. The MLP is the canonical feedforward network, widely popularized through the back-propagation algorithm. Although it lacks explicit temporal recurrency, the MLP remains a strong baseline for various classification tasks due to its universal approximation capability. Figure 5 illustrates the single-hidden-layer MLP architecture used in this study. The input features \((x^{(0)},\ldots,x^{(n)})\) are fully connected to a hidden layer via a weight matrix \(W\) and a bias vector. A Rectified Linear Unit (ReLU) activation function is then applied to produce the hidden representations \(\{h\}\). These activations are passed to the output layer, where another set of parameters maps them to Softmax neurons, yielding class-posterior probabilities. Since the MLP lacks recurrent connections, it cannot capture temporal dependencies. In addition to the models discussed above, a wide range of architectures has been employed in DL-SCA and DL-based cybersecurity research. A common requirement across such studies is the availability of suitable training data. However, in many domains, publicly available datasets remain insufficient to meet the needs of researchers.