  Key components of current cybersecurity methods are the Intrusion Detection
Systems (IDSs) were different techniques and architectures are applied to
detect intrusions. IDSs can be based either on cross-checking monitored events
with a database of known intrusion experiences, known as signature-based, or on
learning the normal behavior of the system and reporting whether some anomalous
events occur, named anomaly-based. This work is dedicated to survey the
application of IDS to the Internet of Things (IoT) networks, where also the
edge computing is used to support the IDS implementation. New challenges that
arise when deploying an IDS in an edge scenario are identified and remedies are
proposed. We focus on anomaly-based IDSs, showing the main techniques that can
be leveraged to detect anomalies and we present machine learning techniques and
their application in the context of an IDS, describing the expected advantages
and disadvantages that a specific technique could cause.
