Fault Diagnosis for IP-Based Networks Using Incremental Learning Algorithms and Data Stream Methods

Network fault diagnosis has evolved in response to the needs of modern networks, transitioning from traditional methods, such as passive and active monitoring, to advanced learning techniques. While conventional methods often introduce invasive traffic and control overhead, newer approaches face challenges such as increased internal processes and the need for extensive knowledge of network behavior. Learning-based methods offer an advantage by not requiring a complete network model, allowing the use of statistical and Machine Learning techniques to process historical data. However, existing learning methods face limitations, such as the need for extensive data samples and extended retraining periods, which can leave systems vulnerable to failures, particularly in dynamic environments. This work addresses these issues by proposing an incremental learning approach for continuous fault diagnosis in IP-based networks. The approach utilizes online learning to process symptoms in real-time, adapting to network changes while managing data imbalance through drift detection and rebalancing strategies, such as ADWIN and SMOTE. We evaluated the performance of this method using 25 incremental algorithms on the SOFI dataset. The results, assessed using metrics such as recall, G-mean, kappa, and MCC, demonstrated high performance over time, indicating the potential for resilient, adaptive fault detection processes in dynamic network environments. Additionally, a non-invasive process can be ensured through peripheral observation of failure symptoms, provided that data collection does not increase network traffic, overhead control, or internal network processes.