## 4 Organizational Experimental Setup
### Experimental Setup 1  
The first experimental setup utilizes \"LeakyReLU\" activation for the hidden layer and \"tanh\" activation for the output layer. The architecture of [PERSON], [PERSON], and [PERSON] is designed to investigate the potential of neural networks to develop a secure communication scheme, without rigidly adhering to any specific cryptographic algorithm. The model chosen for [PERSON] and [PERSON] features a \"concatenate and transform\" design, commencing with a concatenation of the plaintext and key bits, followed by a fully connected (FC) layer with 64 units, where each output can be a linear blend of the input bits, thus encouraging--but not imposing--interplay between the key and plaintext bits subsequent to the FC layer [21]. The Leaky Rectified Linear Unit (LeakyReLU) is a type of activation function that is a variant of the Rectified Linear Unit (ReLU). The formula is shown in Equation (7):  
\[f(x)= \begin{cases}x,\ if\ x>0\\ \alpha x,\ x\leq 0\end{cases}  \tag{7}\]where  
\(x\) is the input to the function. \(\alpha\) is a small positive constant (typically very small, around 0.01) that determines the slope of the function for negative values of \(x\). It is called \"leaky\" because it allows a small gradient when the unit is not active (i.e., when \(x\leq 0\)), which can help mitigate the \"dying ReLU\" problem, where neurons can sometimes get stuck during training and stop updating their weights altogether [22]. In the used method, the following applies:  
1. Relu: this is the standard ReLU function defined as \(f(x)=\max(0,x)\). 2. Sigmoid: this function outputs values between 0 and 1 and is defined as \(s\) in Equation (8). \[f(x)=\frac{1}{1+e^{-x}} \tag{8}\]  
LeakyReLU activation permits a nuanced activation function [23], succeeded by another FC layer, resulting in an output size appropriate for either encrypted or decrypted plaintext, without predefining the transformation function. The architecture for Eve parallels that of Alice and Bob, differing only in the absence of key input. Unlike conventional methods, the design encourages the learning of bit combinations rather than prescribing them [24]. The decision to use LeakyReLU and tanh activation functions was deliberate to offer a more expressive representation. The models refrain from further restrictions, such as tying parameters, to foster an environment where the network learns encryption, decryption, and eavesdropping naturally [25]. The use of LeakyReLU instead of ReLU and the tanh activation function reflects the model architecture. The models are trained over 500,000 steps, with progress being printed every 2000 steps. The training loop generates random messages and keys, encrypts the message, and then decrypts it using both Bob and Eve, computing the losses and updating the models accordingly. Figure 5 shows that starting with an aberrant low at 3.16, the training predominantly showcases execution times clustering around the 18 to 20 s bracket. Although there are sporadic surges reaching up to 22.46 s, the system does not show a clear ascending or descending trajectory. The occasional fluctuations in performance hint at varying computational challenges or dynamic system responses. By the end of the training, a familiar pattern emerges, as performance often falls back to the 18 to 19 s range, suggesting a possible equilibrium or consistent operational capability at this level. Figure 5: Time series analysis for training during experimental setup 1. Figure 6 demonstrates the outcomes of a particularly compelling run, charting the progression of [PERSON]'s reconstruction error and [PERSON]'s reconstruction error against the number of training steps spanning from step 0 to step 500,000 for N = 16-bit plaintext and key pairs, utilizing a minibatch size of 100. Each data point on the curve corresponds to the average error amassed from 100 test instances.