| Title |
BEAST: Behavior as a Service for Trust management in IoT devices |
| ID_Doc |
40964 |
| Authors |
Huber, B; Kandah, F; Skjellum, A |
| Title |
BEAST: Behavior as a Service for Trust management in IoT devices |
| Year |
2023 |
| Published |
|
| DOI |
10.1016/j.future.2023.02.003 |
| Abstract |
As the internet becomes intertwined into every aspect of human life, the security of the Internet of Things (IoT) is also becoming increasingly critical. IoT devices are becoming the primary data source for a variety of smart-city applications, where critical decisions are based on this collected data. If malicious actors gain control of and/or tamper with the data being transmitted, the integrity of an entire smart city will be compromised. However, through monitoring IoT devices' behavior, anomalies can be detected and isolated to avoid any negative impact on decision-making. This behavioral monitoring process will complement traditional trust management approaches, since more accurate trust values can be calculated without the need to rely on a majority consensus. In this work, we present a BEhavior-As-a-Service for Trust management (BEAST) that implements a deep learning -based behavioral model to accurately classify IoT devices' interactions in the system. Through the implementation of the Elo rating system, these classifications will be presented as a vector of behaviors per device, which dynamically reflects each device's trust in the system. This work presents an analysis of our methodology as well as a threat model. Using simulations, a real-world use case is presented showing the interactions between IoT-based devices. Our results show that our BEAST model is able to dynamically evaluate each IoT device's trust, as well as capture and mitigate multiple threats targeting the trust in the system.(c) 2023 Elsevier B.V. All rights reserved. |
| Author Keywords |
Internet of Things; Trust management; Security; Deep learning; Behavioral analysis; Smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000952742500001 |
| WoS Category |
Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
http://manuscript.elsevier.com/S0167739X23000444/pdf/S0167739X23000444.pdf
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