Trustworthiness of Dynamic Moving Sensors for Secure Mobile Edge Computing

Abstract

Wireless sensor network is an emerging technology, and the collaboration of wireless sensors becomes one of the active research areas for utilizing sensor data. Various sensors collaborate to recognize the changes of a target environment, to identify, if any radical change occurs. For the accuracy improvement, the calibration of sensors has been discussed, and sensor data analytics are becoming popular in research and development. However, they are not satisfactorily efficient for the situations where sensor devices are dynamically moving, abruptly appearing, or disappearing. If the abrupt appearance of sensors is a zero-day attack, and the disappearance of sensors is an ill-functioning comrade, then sensor data analytics of untrusted sensors will result in an indecisive artifact. The predefined sensor requirements or meta-data-based sensor verification is not adaptive to identify dynamically moving sensors. This paper describes a deep-learning approach to verify the trustworthiness of sensors by considering the sensor data only. The proposed verification on sensors can be done without having to use meta-data about sensors or to request consultation from a cloud server. The contribution of this paper includes (1) quality preservation of sensor data for mining analytics. The sensor data are trained to identify their characteristics of outliers: whether they are attack outliers, or outlier-like abrupt changes in environments; and (2) authenticity verification of dynamically moving sensors, which was possible. Previous unknown sensors are also identified by deep-learning approach.

Research on Secure Debugging Interaction of Sensor Nodes Based on Visible Light Communication

“…An enhanced trust model based on the radial base artificial neural network (RBANN) was presented by Yasin et al [ 14 ] to predict each node’s future behavior and detect malicious nodes. Yoon et al [ 15 ] provided a DL-based approach to verify the trustworthiness of sensors by considering the sensor data only. These studies focus on applying ML and DL to identify the attack or faulty sensor node and give little consideration to improving the security of communication, not to mention enhancing the security from the physical level.…”

Systematic Review on Security and Privacy Requirements in Edge Computing: State of the Art and Future Research Opportunities

“…The attacker impersonates the physical devices of the legitimate users to propagate malicious effect, steal data, or interfere with edge network access control. In an attempt to solve a spoofing attack, Yoon in [108] ensures that each participating sensor device transmits their data to a legitimate edge node in the Java-Script Object Notation (JSON) format. The wireless sensor networks (WSNs) are requested to send the JSON data about their neighboring sensors.…”

“…The adversary utilizes the advantage of the weaknesses that exist in the physical resources of an edge network before they are discovered by the party responsible for the mitigation exercise. In [108], the authors used an artificial neural network to solve the zero-day attack problems. The tolerance and trigger areas constructed at a training stage are dedicated to ensuring the trustworthiness of the sensors.…”