CANIVTECH Architecture and Usecases

Below are some of the use cases using Machine Learning with Network Data

PREDICT NETWORK FAULTS

High false positives and lack of context are challenges in predicting network faults.
Machine Learning can learn from historical and current data to classify normal and abnormal fault and performance events.

MITIGATE DDOS ATTACKS​

Attackers use changing strategies to initiate DDOS attacks that is challenging to detect with signature based methods
Anomaly detection Machine Learning algorithms can detect them through continuous analysis of hidden patterns in data.

CLASSIFY NETWORK TRAFFIC ​​

Port and payload based methods are insufficient due to increased encrypted traffic, p2p and tunneled applications
Machine Learning classification algorithms can find tangential relationships between data to effectively classify traffic.

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