## 5 Experimental Setup
### Significant Findings  
By analyzing significant findings and comparing the accuracy of default attributes and feature selection for three high-frequency anomaly attacks (DoS, brute-force, and malformed attacks) with respect to seven algorithms (NB, DT, NN, RF, GB, LR, and MLP). Table 3 shows that DT and RF provided high accuracy of 95.94%, 99.81%, and 99.67%, respectively, in all three types of DoS attacks: Brute force attacks and malformed attacks, with no significant difference between default and feature selection attributes when using the correlation technique for DoS attacks. A minor drop in a few of the datasets such as in the Brute force algorithm is due to the removal of attributes in the feature engineering phase. However, this drop is minor in comparison to the advantages achieved in the proposed feature engineering methodology. Furthermore, this can be controlled by adjusting the parameter value of Pearson Correlation Coefficient (r). The performance of a Deep Neural Network (DNN) in detecting various types of cyber attacks. The DNN achieved high accuracy rates in identifying Denial of Service (DoS), Brute Force (BF), and malformed attacks when using default attributes, with accuracy rates of 95.83%, 99.41%, and 99.35%, respectively. The best values for each type of attack are highlighted in bold in Table 3. However, when using Feature Selection (FS) attributes, the accuracy rates were slightly lower, with rates of 87.74%, 99.67%, and 99.10% for DoS, BF, and malformed attacks, respectively. Overall, the DNN demonstrated strong performance in detecting cyber attacks, particularly with default attributes. The results presented in Table 4, which compares the precision and recall of eight different algorithms in detecting various types of cyber attacks (DoS, BF, and Malformed) without Feature Selection (FS). The significant findings of the analysis reveal that the RF and DT algorithms had the highest precision and recall values of 99.80% for detecting Brute Force attacks without the use of FS. This suggests that RF and DT algorithms may be particularly effective for detecting this type of attack. On the other hand, the NB algorithm had the lowest precision value of 89.51% for detecting DoS attacks without FS. This indicates that NB may not be the best choice for detecting this type of attack. For the Malformed attack, the DNN algorithm had the lowest recall value of 76.59% without FS. Overall, the analysis in Table 4 provides insights into the strengths and weaknesses of various algorithms for detecting different types of cyber attacks, and highlights the importance of considering precision and recall values when evaluating the effectiveness of these algorithms. The results presented in Table 5, which compares the precision and recall of eight different algorithms in detecting various types of cyber attacks (DoS, BF, and Malformed) using Feature Selection (FS). The analysis revealed that the RF and DT algorithms had the highest precision and recall values of 99.81% for detecting Brute Force attacks using FS. This suggests that RF and DT algorithms may be particularly effective for detecting this type of attack, even with the use of FS. However, the NB, LR, and DNN algorithms had the lowest precision values of 87.27%, 87.39%, and 87.74%, respectively, for detecting DoS attacks using FS. This indicates that these algorithms may not be the best choice for detecting this type of attack when FS is applied. Overall, the analysis in Table 5 provides further insights into the strengths and weaknesses of various algorithms for detecting different types of cyber attacks, and highlights the importance of considering both FS and precision/recall values when evaluating the effectiveness of these algorithms.