Abstract: Sequences of computer network log entries indicative of a cause of an event described in a first type of entry are identified by training a long short-term memory (LSTM) neural network to detect computer network log entries of a first type. The network is characterized by a plurality of ordered cells Fi=(xi, ci-1, hi-1) and a final sigmoid layer characterized by a weight vector wT. A sequence of log entries xi is received. An hi for each entry is determined using the trained Fi. A value of gating function Gi(hi, hi-1)=II (wT(hi?hi-1)+b) is determined for each entry. II is an indicator function, b is a bias parameter. A sub-sequence of xi corresponding to Gi(hi, hi-1)=1 is output as a sequence of entries indicative of a cause of an event described in a log entry of the first type.

Abstract: Sequences of computer network log entries indicative of a cause of an event described in a first type of entry are identified by training a long short-term memory (LSTM) neural network to detect computer network log entries of a first type. The network is characterized by a plurality of ordered cells Fi=(xi, ci-1, hi-1) and a final sigmoid layer characterized by a weight vector wT. A sequence of log entries xi is received. An hi for each entry is determined using the trained Fi. A value of gating function Gi(hi, hi-1)=II (wT(hi?hi-1)+b) is determined for each entry. II is an indicator function, b is a bias parameter. A sub-sequence of xi corresponding to Gi(hi, hi-1)=1 is output as a sequence of entries indicative of a cause of an event described in a log entry of the first type.