  Kernel traces are sequences of low-level events comprising a name and
multiple arguments, including a timestamp, a process id, and a return value,
depending on the event. Their analysis helps uncover intrusions, identify bugs,
and find latency causes. However, their effectiveness is hindered by omitting
the event arguments. To remedy this limitation, we introduce a general approach
to learning a representation of the event names along with their arguments
using both embedding and encoding. The proposed method is readily applicable to
most neural networks and is task-agnostic. The benefit is quantified by
conducting an ablation study on three groups of arguments: call-related,
process-related, and time-related. Experiments were conducted on a novel web
request dataset and validated on a second dataset collected on pre-production
servers by Ciena, our partnering company. By leveraging additional information,
we were able to increase the performance of two widely-used neural networks, an
LSTM and a Transformer, by up to 11.3% on two unsupervised language modelling
tasks. Such tasks may be used to detect anomalies, pre-train neural networks to
improve their performance, and extract a contextual representation of the
events.
