  Given the increasing complexity of threats in smart cities, the changing
environment, and the weakness of traditional security systems, which in most
cases fail to detect serious threats such as zero-day attacks, the need for
alternative more active and more effective security methods keeps increasing.
Such approaches are the adoption of intelligent solutions to prevent, detect
and deal with threats or anomalies under the conditions and the operating
parameters of the infrastructure in question. This research paper introduces
the development of an intelligent Threat Defense system, employing Blockchain
Federated Learning, which seeks to fully upgrade the way passive intelligent
systems operate, aiming at implementing an Advanced Adaptive Cooperative
Learning (AACL) mechanism for smart cities networks. The AACL is based on the
most advanced methods of computational intelligence while ensuring privacy and
anonymity for participants and stakeholders. The proposed framework combines
Federated Learning for the distributed and continuously validated learning of
the tracing algorithms. Learning is achieved through encrypted smart contracts
within the blockchain technology, for unambiguous validation and control of the
process. The aim of the proposed Framework is to intelligently classify smart
cities networks traffic derived from Industrial IoT (IIoT) by Deep Content
Inspection (DCI) methods, in order to identify anomalies that are usually due
to Advanced Persistent Threat (APT) attacks.
