Paper Title
Fault Classification and Forecasting Using AI in Backbone Networks

Abstract
Unintentional disruptions of Backbone network services, or network failures, cause annoyance to the end service user. This is particularly true for time sensitive services which subsequently result in huge losses. Additionally, there is a growing tendency toward remote working via a dependable network since physical working has become more expensive for businesses worldwide, therefore having a stable network connection is essential. Additionally, network failure costs Backbone Service Providers huge amounts in terms of revenue every year, negatively affecting their ability to operate. For Backbone networks to function flawlessly and without delay, a failure classification and forecasting mechanism is needed to proactively generate alarms for minimising loss of data and revenue. Keywords - Network Failure, Network Failure Detection, Network Availability, AI for Network Faults, Fault Classification and Fault Forecasting