Identification of Machine Learning Algorithms for Anomaly Detection and QoS Assurance

With the increasing complexity of network infrastructures, anomaly detection and Quality of Service (QoS) assurance have become critical in maintaining secure and efficient operations. This paper identifies various machine learning algorithms that contribute to solving these challenges by analyzing network traffic, detecting anomalies, and optimizing QoS. Supervised learning algorithms, such as Support Vector Machines (SVMs), Random Forest, and K-Nearest Neighbors (KNN), have been effectively applied to real-time traffic classification and intrusion detection. These methods excel in identifying patterns that distinguish between legitimate and malicious network activity. Unsupervised techniques, such as K- means clustering, play a crucial role in detecting evolving anomalies without requiring labeled datasets, making them highly adaptable to dynamic environments. Additionally, ensemble learning methods, such as AdaBoost and XGBoost, have shown enhanced performance in both anomaly detection and QoS management through gradient boosting and regularization techniques. This paper explores these algorithms’ capabilities, providing insight into their application in real-world network scenarios and the benefits they bring to anomaly detection and QoS optimization.