  With the proliferation of network devices and rapid development in
information technology, networks such as Internet of Things are increasing in
size and becoming more complex with heterogeneous wired and wireless links. In
such networks, link faults may result in a link disconnection without immediate
replacement or a link reconnection, e.g., a wireless node changes its access
point. Identifying whether a link disconnection or a link reconnection has
occurred and localizing the failed link become a challenging problem. An active
probing approach requires a long time to probe the network by sending signaling
messages on different paths, thus incurring significant communication delay and
overhead. In this paper, we adopt a passive approach and develop a three-stage
machine learning-based technique, namely ML-LFIL that identifies and localizes
link faults by analyzing the measurements captured from the normal traffic
flows, including aggregate flow rate, end-to-end delay and packet loss. ML-LFIL
learns the traffic behavior in normal working conditions and different link
fault scenarios. We train the learning model using support vector machine,
multi-layer perceptron and random forest. We implement ML-LFIL and carry out
extensive experiments using Mininet platform. Performance studies show that
ML-LFIL achieves high accuracy while requiring much lower fault localization
time compared to the active probing approach.
