# Machine Learning-Assisted Secure Random Communication System
## 7 Conclusions  
This study presents the first model that incorporates machine learning into RCSs. The proposed ML-RCS uses SkaSNSK to securely transmit binary information from a transmitter within an AWGN channel. The legitimate receiver uses a pretrained DT with an FT configuration to retrieve the binary bits hidden in the received random carrier signals. Our Monte Carlo simulations analyzed the communication perspectives of both the ML and RCSs. Compared with the previously proposed RCSs, the proposed ML-RCS provides a significant increase in the BER performance. The enhanced BER performance of the ML-RCS is governed by the fact that it incorporates ML via the pretrained DT in order to establish secure communication.  
In future work, we plan to evaluate different ML algorithms to determine the appropriateness of this task. We are currently investigating the applicability of deploying ML algorithms to improve the RCS performance further. We are in the process of producing RCS architectures incorporated within each of the ML algorithms--SVMs, KNNs, and neural Networks--as separate studies. We have adopted this approach to explore the unique characteristics and contributions of each algorithm when integrated into RCSs. In line with the current approaches to designing RCS architectures, this study initially focused on testing the proposed system in an AWGN channel to establish foundational insights and baseline performance metrics. As it was a standard practice in all previous studies, we tested the proposed ML-RCS in a controlled environment like an AWGN channel only so that a fair comparison could be developed. We are currently inspecting the behavior of the proposed ML-RCS in various channel environments. Subsequent efforts will explore the effectiveness of the ML-RCS in scenarios involving Rayleigh-Rician fading and dynamically varying channels to further evaluate its practical viability. Moreover, incorporating already present or developing secure key distribution methods (independent of pilots) for the proposed system will further ensure the establishment of secure communication. It will prevent interception or tampering during synchronization, leading to a stronger foundation for secure communication systems. With the successful integration of ML into RCSs, we expect that ML algorithms will be utilized in other fields. This will help to increase the level of scientific contributions toward a better and more prosperous society.  
Conceptualization, A.A.; Methodology, A.A.; Validation, Z.B.; Formal analysis, A.A.; Investigation, A.A.; Resources, Z.B.; Data curation, A.A.; Writing--original draft, A.A.; Writing--review & editing, Z.B.; Supervision, Z.B.; Project administration, Z.B.; Funding acquisition, A.A. and Z.B. All authors have read and agreed to the published version of the manuscript.  
This publication was supported by the European Union's Horizon Europe Research and Innovation Programme under the Marie Sklodowska-Curie Postdoctoral Fellowship Programme, SMASH cofunded under grant agreement No. 101081355.  
Not Applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.