Anomaly detection using AI/ML for Network Correction

Anomaly detection or outlier detection is a technique to identify rare items, observations or events which are differing significantly from most of the data or do not conform to the expected behavior of the system. Typically, anomalous data cause numerous problems in the computer networking and communication system. This project aims to develop an advanced anomaly detection algorithm by utilizing state-of-the-art machine learning and artificial intelligence techniques and combining it with existing anomaly detection techniques. We propose to develop a unique deep learning methodology based on the Modified Support Vector Machine (MSVM) and the Bi-directional Long Short-Term Memory Recurrent Neural Networks (BLSTM RNN) approaches. We will test and evaluate the solution with respect to the accuracy, miscalculation rate, precision, true positive rate and F1 score

Faculty Supervisor:

Kin-Choong Yow


Chi Mai Kim Ho


Ericsson Canada





University of Regina



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