  Energy providers are moving to the smart meter era, encouraging consumers to
install, free of charge, these devices in their homes, automating consumption
readings submission and making consumers life easier. However, the increased
deployment of such smart devices brings a lot of security and privacy risks. In
order to overcome such risks, Intrusion Detection Systems are presented as
pertinent tools that can provide network-level protection for smart devices
deployed in home environments. In this context, this paper is exploring the
problems of Advanced Metering Infrastructures (AMI) and proposing a novel
Machine Learning (ML) Intrusion Prevention System (IPS) to get optimal
decisions based on a variety of factors and graphical security models able to
tackle zero-day attacks.
