Proactive detection of network failures and performance degradations is a key to rapid fault recovery and thus robust networking. In this paper, we present methodologies and algorithms that we developed in order to enhance the proactive and adaptive detection of network/service anomalies (failures and performance degradations) in transaction-based Electronic Commerce Wide Area Networks (WANs). Specifically our proactive network/service anomaly detection method detects network/service performance degradations and failures in multiple service-class networks, where performances of service classes are mutually dependent and strongly correlated, and where external or environmental factors (e.g., non-managed or non-monitored equipment within customer premises) can strongly impact network and service performances. In this paper we describe and implement algorithms that (1) sample and convert raw transaction records to service-class based performance data in which potential network anomalies are highlighted, (2) construct adaptive and service-class based performance thresholds for real-time detection of network and service anomalies, and (3) perform real-time network anomaly detection.