TITLE: A Survey on Device Behavior Fingerprinting: Data Sources, Techniques, Application Scenarios, and Datasets 

ABSTRACT: In the current network-based computing world, where the number of
interconnected devices grows exponentially, their diversity, malfunctions, and
cybersecurity threats are increasing at the same rate. To guarantee the correct
functioning and performance of novel environments such as Smart Cities,
Industry 4.0, or crowdsensing, it is crucial to identify the capabilities of
their devices (e.g., sensors, actuators) and detect potential misbehavior that
may arise due to cyberattacks, system faults, or misconfigurations. With this
goal in mind, a promising research field emerged focusing on creating and
managing fingerprints that model the behavior of both the device actions and
its components. The article at hand studies the recent growth of the device
behavior fingerprinting field in terms of application scenarios, behavioral
sources, and processing and evaluation techniques. First, it performs a
comprehensive review of the device types, behavioral data, and processing and
evaluation techniques used by the most recent and representative research works
dealing with two major scenarios: device identification and device misbehavior
detection. After that, each work is deeply analyzed and compared, emphasizing
its characteristics, advantages, and limitations. This article also provides
researchers with a review of the most relevant characteristics of existing
datasets as most of the novel processing techniques are based on machine
learning and deep learning. Finally, it studies the evolution of these two
scenarios in recent years, providing lessons learned, current trends, and
future research challenges to guide new solutions in the area.
In the current network-based computing world, where the number of
interconnected devices grows exponentially, their diversity, malfunctions, and
cybersecurity threats are increasing at the same rate. To guarantee the correct
functioning and performance of novel environments such as Smart Cities,
Industry 4.0, or crowdsensing, it is crucial to identify the capabilities of
their devices (e.g., sensors, actuators) and detect potential misbehavior that
may arise due to cyberattacks, system faults, or misconfigurations. With this
goal in mind, a promising research field emerged focusing on creating and
managing fingerprints that model the behavior of both the device actions and
its components. The article at hand studies the recent growth of the device
behavior fingerprinting field in terms of application scenarios, behavioral
sources, and processing and evaluation techniques. First, it performs a
comprehensive review of the device types, behavioral data, and processing and
evaluation techniques used by the most recent and representative research works
dealing with two major scenarios: device identification and device misbehavior
detection. After that, each work is deeply analyzed and compared, emphasizing
its characteristics, advantages, and limitations. This article also provides
researchers with a review of the most relevant characteristics of existing
datasets as most of the novel processing techniques are based on machine
learning and deep learning. Finally, it studies the evolution of these two
scenarios in recent years, providing lessons learned, current trends, and
future research challenges to guide new solutions in the area.
A Survey on Device Behavior Fingerprinting: Data Sources, Techniques, Application Scenarios, and Datasets
http://arxiv.org/abs/2008.03343v2