  Analysis of large observational data sets generated by a reactive system is a
common challenge in debugging system failures and determining their root cause.
One of the major problems is that these observational data suffer from
survivorship bias. Examples include analyzing traffic logs from networks, and
simulation logs from circuit design. In such applications, users want to detect
non-spurious correlations from observational data and obtain actionable
insights about them. In this paper, we introduce log to Neuro-symbolic
(Log2NS), a framework that combines probabilistic analysis from machine
learning (ML) techniques on observational data with certainties derived from
symbolic reasoning on an underlying formal model. We apply the proposed
framework to network traffic debugging by employing the following steps. To
detect patterns in network logs, we first generate global embedding vector
representations of entities such as IP addresses, ports, and applications.
Next, we represent large log flow entries as clusters that make it easier for
the user to visualize and detect interesting scenarios that will be further
analyzed. To generalize these patterns, Log2NS provides an ability to query
from static logs and correlation engines for positive instances, as well as
formal reasoning for negative and unseen instances. By combining the strengths
of deep learning and symbolic methods, Log2NS provides a very powerful
reasoning and debugging tool for log-based data. Empirical evaluations on a
real internal data set demonstrate the capabilities of Log2NS.
