## 4 Experiments and Their Outcomes
### Comparing the Proposed System to Existing Systems and Qualitative Evaluation  
This section compares the proposed approach to benchmark studies. It is challenging to do a real comparison of the stated procedures due to a variety of constraints. For example, such algorithms are evaluated on a variety of datasets, making comparison complicated. Furthermore, the participating researchers offer the methodologies in their studies at an abstract level with little information, which could render them unfeasible for future investigations.  
Bearing the aforesaid difficulties in mind, we implemented the strategies outlined in the published works using two datasets. During implementation, we did our best to adhere to the original experiment and procedure described in the papers; nevertheless, owing to inadequate discussions and an absence of adequate information in certain cases, we had to remove such aspects of the technique or presume what the researchers wanted. For example, using historical traffic, Ref. [1] suggested a supervised ML model for predicting DDoS attacks. On historical traffic, an ML algorithm called Multiple Linear Regression was used. The model's performance is poor, as evidenced by the experimental results (accuracy: 75%, precision: 75%, recall: 75%, and F1-score: 75%), obtained on the given benchmark dataset. However, they did not indicate specifications on system parameters and feature engineering, which may differ from the authors.  
We conducted a quantitative evaluation of the various DDoS attacks detection algorithms using two cutting-edge datasets obtained from [27]. We used an Anaconda-based Jupyter notebook to apply known methodologies [12]. The findings from our tests diverge from the stated results in a few instances owing to the use of various datasets, parameterization, and software. For example, although [PERSON] and [PERSON] [1] claimed 85 percent accuracy, we achieved 75 percent; [PERSON]. [9; 12] reported 84 percent accuracy, while our studies generated 74 percent on the CICDDoS2019 dataset and 77 percent on the CIC-IDS2017 dataset. The variations in the claimed and tested accuracies were induced by the authors' utilization of multiple datasets. In the study on DDoS traffic data, [PERSON]. [12] used the DL method. It was discovered that combining better feature selection approaches with a DL model might increase the model's efficacy. In another work, [PERSON]. [11] proposed a supervised DL model for predicting DDoS attacks based on historical traffic. A deep learning method called the feed forward model was employed to analyze historical traffic. The model's performance on the provided benchmark dataset demonstrated low results in the absence of an optimal set of features. In our implementation, a hybrid deep learning model, especially CNN + BiLSTM with an improved FS, outperformed earlier methodologies, and it is recommended that more research into various combinations of deep learning models for predicting DDoS attacks will yield more remarkable results. The suggested DL-based solution for predicting DDoS attacks was based on a hybrid deep neural network model and an enhanced feature selection strategy. The experimental findings show that the suggested approach outperforms baseline research (Table 13), and that the selected predictor factors (10) have a substantial impact on the projected (target) variable.