## 6 Conclusions  
In order to improve IoT platform security, this study proposed the novel GA-HDLAD model for the automatic detection of botnets. This method addresses the security challenges in IoT systems by recognizing botnets effectively. The GA-HDLAD model integrates three key components: HDL-based classification, a GA-based feature selection subset, and hyperparameter tuning using a SA approach. To mitigate the issue of high dimensionality, the GA-HDLAD algorithm utilizes the GA for feature selection. HDL also plays a crucial role in botnet detection, incorporating advanced techniques such as FET, RNNs, and attention mechanisms. The HDL model further optimizes its parameters through SA, improving the detection accuracy. Experiments conducted using a benchmark botnet dataset demonstrated that the GA-HDLAD method outperforms several recently developed detection models.  
However, this study has certain limitations that should be acknowledged. One limitation is that the performance of the GA-HDLAD model was not compared directly with other algorithms used in IoT anomaly detection. Additionally, while the model was validated using the CTU-13 dataset, no comparison was made with other datasets commonly used in botnet detection, which could have provided more comprehensive insights into the model's generalizability. Future work should include comparative studies between the GA-HDLAD model and other popular detection algorithms, such as support vector machines, decision trees, or simpler neural networks, to better evaluate the model's performance in various scenarios.  
The computational complexity of the GA-HDLAD model is another limitation, particularly due to the integration of multiple advanced techniques such as GAs, FET, RNNs, and attention mechanisms. This may affect the scalability of the model in resource-constrained environments. Future research should focus on optimizing the model for deployment in low-resource IoT platforms, possibly through model compression techniques or lightweight architectures.  
Additionally, future studies should explore the use of different datasets to assess the model's robustness and adaptability to real-world IoT environments. A more detailed comparison of the computational overhead and performance across different datasets and algorithms will provide a clearer understanding of the GA-HDLAD model's strengths and limitations.  
Further work will also focus on exploring additional improvements, such as refining the attention mechanism for improved feature focus and investigating alternative neural network architectures to enhance performance. Ablation studies can also be conducted to measure the individual contributions of FET, RNNs, and attention mechanisms within the HDL framework.  
This research was funded by the Researchers Supporting Project number (RSP2024R233), King Saud University, Riyadh, Saudi Arabia.  
Data Availability StatementData can be made available upon request to ensure that privacy restrictions are upheld.  
The author would like to extend his sincere appreciation to the Researchers Supporting Project (RSP2024R233), King Saud University, Riyadh, Saudi Arabia.  
The author declares no conflicts of interest.  
References