## 4 Organizational Experimental Setup
### Experimental Setup 2  
The second experimental setup utilizes \"Mish\" activation for the hidden layer and \"sigmoid\" activation for the output layer. This model, designed for [PERSON] and [PERSON], adopts a \"concatenate and transform\" paradigm [27]. The process starts by concatenating the plaintext message with the cryptographic key. This amalgamated input then flows through a fully connected (FC) layer encompassing 64 units. In this FC layer, the output can potentially be a sophisticated blend of the input bits. This design implicitly promotes interaction between the plaintext and the key, allowing the encryption process to naturally use the key's information. Following this initial transformation, the data is subjected to the Mish activation function--a smooth, non-linear function derived from the combination of the \(\tanh\) and softplus functions [28,29]. The hyperbolic tangent function, often abbreviated as \(\tanh\), is defined as in Equation (9):  
\[\tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{9}\]  
This function maps any real-valued number to the interval \([-1,1]\). It is zero-centered, which means that negative inputs will be mapped strongly negative and the zero inputs will be near zero in the output. The derivative of softplus with respect to x is defined in Equation (10):  
\[\frac{d}{dx}softplus(x)=\ln(1+e^{x}) \tag{10}\]  
which is essentially the sigmoid function. Given the two functions defined above, the Mish activation function is defined in Equation (11):  
\[mish(x)=x*\tanh(softplus(x))\;\;\text{or}\;mish(x)=x*\;\tanh(\ln(1+e^{x})) \tag{11}\]  
This choice facilitates a nuanced activation mechanism, making the transformation more expressive and flexible. Post activation, the transformed data are directed to another FC layer, which returns an output of size equivalent to the original message length [30]. This output represents the encrypted (or decrypted, in [PERSON]'s case) message. The choice of the sigmoid activation function for this layer ensures the output values are constrained between 0 and 1, fitting the requirements of an encrypted message [31]. Figure 7: A line graph tracking the increasing cumulative time, in seconds, for every 2000 training steps up to 500,000, showcasing the escalating time commitment as training advances during experimental setup 1. The architecture for [PERSON] mirrors the blueprint used for [PERSON] and [PERSON], with one crucial difference: it does not account for the cryptographic key [32]. [PERSON]'s task is to decipher the encrypted message without knowledge of this key. Consequently, her model starts directly with the encrypted message, then proceeds through a series of transformations akin to those in [PERSON] and [PERSON]'s networks, sans the concatenation step. Figure 8 demonstrates the outcomes of a particularly compelling run employing the Mish and sigmoid activation functions, exemplifying the sophistication of the model's architecture. The intensive training spans half a million steps, with milestones echoed every 2000 intervals till 500,000 steps. Throughout this rigorous loop, the system dynamically crafts random messages and keys, processes encryption, and engages both [PERSON] and [PERSON] in decryption, adeptly calculating losses and optimizing the models in response. Both the Alice-Bob and Eve loss values start relatively high, indicating that initially, the models are not performing well. As the steps increase, both losses seem to decrease. Upon examining the time taken for various computational steps, ranging from 0 to 500,000 in increments of 2000, it is evident that after an initial rapid change, the time duration stabilizes around an average of approximately 20.52 s. The variance in time taken after this initial change is minimal, indicating a consistent computational efficiency across the steps. This consistency suggests that, within the scope of the experiment, the computational task scales linearly with an increase in the number of steps.