Using neural networks to identify control and management plane poison messages

Poison message failure propagation is a mechanism that has been responsible for large scale failures in both telecommunications and IP networks: some or all of the network elements have a software or protocol ´bug´ that is activated on receipt of a certain network control/management message (the poison message). This activated ´bug´ will cause the node to fail with some probability. If the network control or management is such that this message is persistently passed among the network nodes, and if the node failure probability is sufficiently high, large-scale instability can result. Identifying the responsible message type can permit filters to be configured to block poison message propagation, thereby preventing instability. Since message types have distinctive modes of propagation, the node failure pattern can provide valuable information to help identify the culprit message type. Through extensive simulations, we show that artificial neural networks are effective in isolating the responsible message type.