## 4 Evaluation and Performance Optimization
### Feature Engineering
RF-GAORed's accuracy decreased further to 20%, indicating significant challenges in detecting advanced attacks under the modified configuration, while, with the XGBoost-GAORed, despite the complexity of ZooAttack with LightGBMClassifier, the modified XGBoost achieved a perfect 100% accuracy, suggesting that the modification enhanced its ability to detect and mitigate advanced adversarial strategies. Our observations reveal that all classifiers performed well on ZooAttack with XGBoost-Classifier, including the baseline, suggesting that standard adversarial attacks have clear and distinguishable patterns. GAORed classifiers negatively impacted their ability to detect these attacks, possibly due to overfitting or altered feature extraction. The inability of \"No Model\" to detect attacks highlights the advanced nature of ZooAttack with LightGBMClassifier, where patterns are obfuscated and require a robust classifier. XGBoost demonstrated greater resilience and adaptability compared to RF, excelling even after modification, possibly due to its gradient-boosting architecture handling intricate patterns effectively. For RF, GAORed significantly reduced detection capabilities with both classifiers, suggesting that the changes were counterproductive. XGBoost's GAORed version, however, adapted well to ZooAttack with LightGBMClassifier, improving its effectiveness against advanced adversarial attacks. Consequently, the results underline the need for tailored classifiers to address different types of adversarial attacks. XGBoost emerges as the more reliable model, particularly in handling complex adversarial strategies, and benefits from strategic modification, while RF performs well for simpler attacks but requires careful tuning for adversarial robustness in sophisticated scenarios. Effective adversarial attack classification \begin{table}
\begin{tabular}{l l} \hline \hline
**Learning Approach** & **Features** \\ \hline Random Forest (without GA) & ‘Protocol’, ‘Total Fwd Packets’, ‘Total Backward Packets’, ‘Fwd Packets Length Total’, ‘Bwd Packets Length Total’, ‘Fwd Packet Length Max’, ‘Fwd Packet Length Min’, ‘Fwd Packet Length Mean’, ‘Flow IAT Men’, ‘Flow IAT Stat’, ‘Flow IAT Max’, ‘Fwd IAT Total’, ‘Fwd IAT Mean’, ‘Fwd IAT Std’, ‘Fwd IAT Max’, ‘Bwd IAT Min’, ‘Bwd Packets/s’, ‘Packet Length Min’, ‘Packet Length Max’, ‘Packet Length Mean’, ‘ACK Flag Count’, ‘URG Flag Count’, ‘CWE Flag Count’, ‘Down/Up Ratio’, ‘Avg Packet Size’, ‘Avg Fwd Segment Size’, ‘Subflow Fwd Packets’, ‘Subflow Fwd Packets’, ‘Subflow Fwd Bytes’, ‘Init Fwd Win Bytes’, ‘Subflow Bwd Bytes’, ‘Init Fwd Win Bytes’, ‘Fwd Act Data Packets’, ‘Idle Mean’, ‘Idle Std’, ‘Idle Max’, ‘Idle Min’ \\ \hline Random Forest (with GA) & ‘Protocol’, ‘Total Fwd Packets’, ‘Total Backward Packets’, ‘Fwd Packet Length Max’, ‘Fwd Packet Length Min’, ‘Fwd Packet Length Std’, ‘Bwd Packet Length Min’, ‘Flow Packets/s’, ‘Flow IAT Mean’, ‘Flow IAT Std’, ‘Fwd IAT Total’, ‘Fwd IAT Mean’, ‘Fwd IAT Max’, ‘Bwd IAT Min’, ‘Packet Length Min’, ‘Packet Length Max’, ‘Packet Length Std’, ‘ACK Flag Count’, ‘URG Flag Count’, ‘Down/Up Ratio’, ‘Avg Packet Size’, ‘Subflow Fwd Bytes’, ‘Init Fwd Win Bytes’, ‘Init Bwd Win Bytes’, ‘Active Min’, ‘Idle Std’, ‘Idle Max’, ‘Idle Min’ \\ \hline XGBoost (without GA) & ‘Protocol’, ‘Flow Duration’, ‘Total Backward Packets’, ‘Fwd Packet Length Min’, ‘Flow IAT Mean’, ‘Bwd Header Length’, ‘Fwd Packets/s’, ‘Bwd Packets/s’, ‘Packet Length Min’, ‘ACK Flag Count’, ‘URG Flag Count’, ‘Active Min’, ‘Idle Std’ \\ \hline XGBoost (with GA) & ‘Flow IAT Mean’, ‘Packet Length Min’, ‘ACK Flag Count’, ‘URG Flag Count’, ‘URG Flag Count’, ‘Down/Up Ratio’, ‘Init Fwd Win Bytes’, ‘Idle Std’, ‘Idle Max’ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Important features using the CIC-IDS2019 dataset. depends on understanding the attack vectors and selecting classifiers optimized for the dataset's complexity.