Modeling and Prediction of Network Failures: A Machine Learning Approach

Network failures remain a critical challenge for Internet Service Providers (ISPs), often causing severe service disruptions that lead to significant financial losses, customer dissatisfaction, and business interruptions. This issue has been worsened by the shift towards digital services, particularly during the COVID-19 pandemic, where continuous internet availability became vital. Despite the high costs associated with network failures, ISPs lack a comprehensive failure identification and prediction system, primarily due to the scarcity of publicly available failure datasets. To address this gap, our research generates a synthetic failure dataset [1] through network traffic simulation, adhering to a comprehensive set of network failure guidelines provided by Cisco. The dataset captures various failure types, including hardware (e.g., CPU, memory, card, port) and software failures (e.g., OS misconfiguration, upgrades), based on real-world failure patterns observed in network infrastructures. We simulate traffic for different network topologies over a multi-year period to replicate real-world conditions, incorporating features such as port utilization, card risks, and location-based factors that contribute to failures. Using this synthetic dataset, we applied several state-of-the-art Machine Learning and Deep Learning algorithms to develop a reliable failure identification and prediction framework. Deep Learning model achieved exceptional results compared to machine learning model, with accuracy, precision, recall, and F1 scores ranging from 97% to 99% across multiple network topologies and failure categories. This work demonstrates that predictive techniques can significantly improve network reliability by reducing downtime and optimizing the use of redundant network resources.