Derin öğrenme aracılığıyla hibrit tespit geliştirmeleriyle ağ ve sunucu giriş tespiti

Network security and the identification of possible threats depend heavily on anomaly detection in network traffic. In order to determine which machine learning models are most useful in detecting network traffic anomalies, this research compared and evaluated a number of different machine learning models. Deep Learning with 10 epochs, Logistic Regression, Random Forest with a Filter method, Random Forest with a Wrapper method, and Random Forest with a Wrapper method were the models evaluated. A number of metrics were used for evaluation, including cross-validation, accuracy, F1-Score, ROC (Receiver Operating Characteristic), and precision- recall. The results showed that the Random Forest with the Wrapper approach and the Deep Learning model outperformed other assessment criteria. With a ROC score of 0.9716, the Deep Learning model had the best performance, clearly demonstrating its superior ability to differentiate between regular and abnormal network traffic. It also displayed outstanding precision-recall scores of 0.9621, indicating accurate anomaly identification with a low incidence of false positives. The Deep Learning model also attained a strong F1-Score of 0.8405, which represents a balanced mix of recall and precision.