## 1 Introduction
### Paper Contributions and Organization  
Based on the aforementioned research gaps and the importance of EE and secure communication in CF m-MIMO-enabled IoT networks, the main contributions of this paper are summarized as follows:  
* This work employs SEE as a key performance metric to jointly optimize both EE and SR in CF m-MIMO-based IoT networks. Unlike traditional approaches that focus on either EE, QoS, or security, our framework ensures a balanced trade-off by quantifying the impact of power consumption on secure communication.
* We propose a novel hybrid deep learning (DL) model based on a convolutional neural network (CNN) and long-short-term memory (LSTM) to improve SEE optimization in CF m-MIMO. CNN is responsible for extracting spatial features from the IoT network environment, while the LSTM network captures temporal dependencies, allowing the system to adapt dynamically to changing network conditions and security threats.
* To further improve the efficiency and effectiveness of the deep-learning framework, we incorporate a multi-objective improved biogeography-based optimization (MOIBBO) algorithm for hyperparameter tuning. The MOIBBO improves training performance by optimizing key model parameters, accelerating convergence, and improving both classification accuracy and model robustness to secure IoT communications.
* We conduct an extensive simulation and performance evaluation comparing our proposed model with existing CF m-MIMO security and energy efficiency frameworks. The simulation results demonstrate that our approach achieves higher SEE, lower power consumption, and better security performance compared to conventional methods.  
The remainder of this paper is organized as follows: Section 2 describes the system model and problem formulation for SEE maximization in CF m-MIMO-enabled IoT networks. Section 3 presents the materials and methodology of the proposed hybrid DL framework. Section 4 provides the simulation results, assessing the performance of the proposed solution. Section 5 presents the discussion and comparison of the results with benchmark approaches, and finally, Section 6 concludes the paper.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Work** & **EE** & **SE** & **AB** & **PLS** & **SEE** & **TO** \\ \hline [PERSON] et al. [28] & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [29] & ✓ & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [30] & ✓ & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [31] & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [32] & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [33] & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [34] & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [35] & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [36] & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [37] & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [38] & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [39] & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline This Work & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of Related Works Based on Key Evaluation Metrics.