Proactive computer network monitoring based on homogeneous deep neural ensemble - Ramiz Shikhaliyev; Lyudmila Sukhostat (Results in Control and Optimization, 2023)
Computer networks are getting more complex these days. A computer network failure can result in the loss of important data, disruption of network services and applications, and economic loss and threaten national security. Therefore, it is crucial to detect failures on time and diagnose their root cause, which is possible with the help of proactive computer network monitoring Proactive computer network monitoring requires effective anomaly detection methods. The paper proposes a conceptual model of a system for proactive computer network anomaly detection. To achieve high prediction accuracy, we propose to use a homogeneous ensemble, which consists of a base learning algorithm. An ensemble of deep neural networks based on base learning three-layered LSTM models was created using the bagging algorithm. We use the CICIDS2017 dataset to evaluate the proposed approach. Experimental results show that our method effectively improves the accuracy of anomaly prediction in computer networks.