Machine Learning Empowered Trust Evaluation Method for IoT Devices

Abstract

With the rapid development of the Internet of Things (IoT), malicious or affected IoT devices have imposed enormous threats on the IoT environment. To address this issue, trust has been introduced as an important security tool for discovering or identifying abnormal devices in IoT networks. However, evaluating trust for IoT devices is challenging because trust is a degree of belief with regard to various types of trust properties and is difficult to measure. Thus, a machine learning empowered trust evaluation method is proposed in this paper. With this method, the trust properties of network QoS (Quality of Service) are aggregated with a deep learning algorithm to build a behavioral model for a given IoT device, and the timedependent features of network behaviors are fully considered. Trust is also quantified as continuous numerical values by calculating the similarity between real network behaviors and network behaviors predicted by this behavioral model. Trust values can indicate the trust status of a device and are used for decision making. Finally, the proposed method is verified with experiments, and its effectiveness is described.

Trusted and secure composite digital twin architecture for collaborative ecosystems

“…Therefore, trust must be analysed before starting communication and throughout the operation to ensure all connected DTs operate as expected. Examples of computation-based trust include quality of service (QoS) analysis [32,[47][48][49], ranking, and reputation management based on past behaviours [17,37,38,48,49].…”

“…Reference [48] proposes an approach utilising operative contexts to analyse trust. There are three operative contexts: user, resource, and organisation.…”

“…This is more aligned with the organisation's legal capabilities to take responsibility for its actions. Even though this study highlights other potential requirements that can be considered, it only uses resource operative context (QoS) for its analysis [48].…”

Automation and Orchestration of Zero Trust Architecture:Potential Solutions and Challenges

“…The rapid processing of big data by AI makes AI-based trust evaluation methods suitable for existing complex network application scenarios. Online social networks (OSN) is one direct way to obtain user features, Supervised learning classifies users into trustworthy and untrustworthy by treating trust evaluation as a classification problem [166,27,26], or quantifies trust values directly using continuous values [158,40,93] based on OSN users' features.…”

DMKT: Dynamic Mining for Proof of Working Based on KNN Trust Model

“…However, the detection of abnormal traffic data cannot be feedback in a timely manner. To address the issue of traffic anomaly detection, Ma et al [19]propose using machine learning to analyze the behavior of devices in the IoT. However, this approach cannot ensure the safety and reliability of the data sources collected by sensors in the IoT system, and thus requires further improvement.…”