## 4 Experimental Results  
### Experimental Setup  
The effectiveness of the EEDPN was experimentally assessed, as shown in this section. This section contains detailed descriptions of the evaluation dataset, the experimental settings, and the experimental methods, as well as comparisons to traditional approaches, imbalanced data-processing algorithms, and cutting-edge intrusion detection techniques. To ensure efficient data handling and model training, hardware with the following specifications was used. An Intel Core i7 8 th Gen processor (or higher) with a clock speed of at least 3.5 GHz was used. An NVIDIA GeForce RTX 2080 Ti GPU with 11 GB of GDDR6 VRAM was used due to its excellent deep-learning performance. The system has 16 GB of DDR4 RAM (2400 MHz) and a high-speed SSD with a 500 GB storage capacity for storing datasets, code, and model checkpoints. To support the specified hardware tools, the following software was used. The system operates on Windows 10 (64-bit). Keras 2.13 with TensorFlow as a backend for deep learning model development and MATLAB R2023b with the Statistics and Machine Learning Toolbox for machine learning algorithms were installed. Python 3.7 was used for data preprocessing and analysis. NumPy (v1.18.5) was used for numerical computations, pandas (v1.0.5) was used for data manipulation, scikit-learn (v0.23.1) was used for machine learning tasks, Matplotlib (v3.2.2) was used for data visualization, and Jupyter Notebook (v6.0.3) was used as the integrated development environment (IDE) for coding and experimentation. At this stage, emerging benchmarking datasets like UNSW-NB 15 (intrusion dataset IS-1) and NSL-KDD (intrusion dataset IS-2) were taken into consideration for testing and validating this system. The Australian Centre for Cyber Security (ACCS) produced the UNSW-NB15 dataset in 2015. It comprises a wide range of deep-structure network communication data as well as minimal incursion information. As a result, it is better suited to imitating the complicated modern network environment. It depicts the modern network traffic mode. It has one unique attack category designation and 47 attributes. There are 2,540,044 samples in the collection, representing nine different attack methods, including fuzzers, DoS, analysis, reconnaissance, exploitation, shellcode, worm, backdoor, and generic. The NSL-KDD and UNSW-NB15 datasets, which exhibit high feature dimensions along with substantial data volume, are typical examples of high-dimensional imbalanced datasets. The majority of the data contained in them are typical network data, with only a trace quantity of attack data. Lower detection accuracy and longer training and detection times are precipitated by duplicated features and unbalanced data. The test set encompasses unidentified attacks, which puts the capacity for generalization under greater strain. Descriptions of the datasets are given in Table 2.  
The NSL-KDD Dataset requires approximately 2 GB of storage space, while the UNSW-NB15 Dataset, being larger, requires around 4 GB of storage space. During deep-learning model training, the GPU memory usage ranges from 6 GB to 10 GB. This is to accommodate the model's architecture and batch size. The NVIDIA GeForce RTX 2080 Ti provides ample memory for efficient training. The bulk of CPU usage primarily occurs during data preprocessing, where multiple CPU cores are used for parallel data processing. The extent of CPU utilization varies but typically stays below 50%. RAM usage during model training depends on the batch size and model complexity. With a batch size of 32-64, the RAM usage remains within the available 16 GB, ensuring smooth model training. The training time for the EFDPN model using the specified hardware ranges from several hours to a day. This is related to the dataset's size, model complexity, and the number of training epochs. Adequate storage space (500 GB) is available for storing datasets, code, model checkpoints, and experiment results.