  Reaping the benefits of the Internet of things (IoT) system is contingent
upon developing IoT-specific security solutions. Conventional security and
authentication solutions often fail to meet IoT security requirements due to
the computationally limited and portable nature of IoT objects. In this paper,
an IoT objects authentication framework is proposed. The framework uses
device-specific information, called fingerprints, along with a transfer
learning tool to authenticate objects in the IoT. The framework tracks the
effect of changes in the physical environment on fingerprints and uses unique
IoT environmental effects features to detect both cyber and cyber-physical
emulation attacks. The proposed environmental effects estimation framework is
proven to improve the detection rate of attackers without increasing the false
positives rate. The proposed framework is also shown to be able to detect
cyber-physical attackers that are capable of replicating the fingerprints of
target objects which conventional methods are unable to detect. A transfer
learning approach is proposed to allow the use of objects with different types
and features in the environmental effects estimation process to enhance the
performance of the framework while capturing practical IoT deployments with
diverse object types. Simulation results using real IoT device data show that
the proposed approach can yield a 40% improvement in cyber emulation attacks
detection and is able to detect cyber-physical emulation attacks that
conventional methods cannot detect. The results also show that the proposed
framework improves the authentication accuracy while the transfer learning
approach yields up to 70% additional performance gains.
