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

Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing and Machine Learning