Trust management and evaluation for edge intelligence in the Internet of Things (original) (raw)

Survey on Trust Calculation Methods in Internet of Things

Procedia Computer Science, 2019

The Internet of Things (IoT) is widely affecting our daily lives in many different activities and applications. IoT devices are ranging from a tiny wearable device to large industrial applications. Wide variety of IoT applications have been developed and deployed involving many devices and produced huge data. In IoT applications, privacy, security, and trust play an important role in the success of IoT implementation. Trust can be defined as a key property to establish trustworthy and connectivity among devices to ensure secure services and applications. This paper addresses a survey of trust calculation models for IoT systems, i.e., what are available models or methods used by researcher to compute trust in IoT system. In addition, classification is also developed to categorize trust calculation model using five parameters including trust metric, trust source, trust algorithm, trust architecture, and trust propagation. Furthermore, some research challenges and directions are identified within the topic of trust-based security in IoT.

TRUST BASED ROUTING ALGORITHM IN INTERNET OF THINGS (IoT)

March 2019

The development in the area of networking is Internet of Things (IoT). This will interrelated the object and things together. The realization of IoT subsystems will be subjected to numerous constraints that include cost, power, energy, and lifetime. However, most challenging requirement will be trust. It is widely recognized that the attacks from malicious parties can activate from Internet to the physical word. Hence, trust of IoT is of essential importance. Therefore, trust management is considered as a efficient solution to IoT related issues. Trust management has useful technology for providing security service and it has been used in many applications such as P2P, Grid, adhoc network and so on. Thus the trust based routing algorithm in Internet of Things is proposed for providing a potential security system. With this, the major focus on the problem of trust on the malicious nodes in any environment.

A Comprehensive Study on the Trust Management Techniques in the Internet of Things

IEEE Internet of Things Journal, 2019

Internet of Things (IoT) has been developed as one of the most significant technology in the future of the Internet, in which the physical objects are transformed into smart objects that can be handled and monitored via the Internet. The trust management takes a significant role in the IoT for enabling trustworthy data collection, context-awareness, and enhanced user privacy. Despite the critical significance of trust management techniques in the IoT, there is not any organized and comprehensive study in this field. Therefore, the aim of this paper is to review the available methods in this field in a systematic way. In this regard, the selected techniques are categorized into four main classes, including recommendation-based, prediction-based, policy-based, and reputation-based. Then they are discussed and also compared based on some trust metrics such as accuracy, adaptability, availability, heterogeneity, integrity, privacy, reliability, and scalability. Furthermore, some hints and challenges for further studies are outlined.

A Smart Trust Management Method to Detect On-Off Attacks in the Internet of Things

Security and Communication Networks

Internet of Things (IoT) resources cooperate with themselves for requesting and providing services. In heterogeneous and complex environments, those resources must trust each other. On-Off attacks threaten the IoT trust security through nodes performing good and bad behaviors randomly, to avoid being rated as a menace. Some countermeasures demand prior levels of trust knowledge and time to classify a node behavior. In some cases, a malfunctioning node can be mismatched as an attacker. In this paper, we introduce a smart trust management method, based on machine learning and an elastic slide window technique that automatically assesses the IoT resource trust, evaluating service provider attributes. In simulated and real-world data, this method was able to identify On-Off attackers and fault nodes with a precision up to 96% and low time consumption.

Effective distributed trust management model for Internet of Things

Procedia Computer Science, 2018

The IoT (Internet of Things) is defined as a global infrastructure for the information society, which provides advanced services by interconnecting physical or virtual objects through existing interoperable information and communication technologies in evolution. To note, the explosion of the number of smartphones and connections has created a new market with almost infinite opportunities. In 2016, 5.5 million objects are connected every day in the world. A number that could quickly reach billions, by 2020 [1]. Gartner predicts that 26 billion objects will be installed in 2020. Other evaluations consider that a human being would interact with 1,000 to 5,000 objects during a normal day. The market for connected objects could range from a few tens of billions to up to several thousand billion units. Among the vital components of IoT, we find wireless sensor networks. WSNs allow the representation of dynamic characteristics of the real world in the virtual world of the Internet. Nevertheless, the opening of these types of network to the Internet presents a serious problem stand point security. The introduction of intrusion detection mechanisms is essential to limit the various attacks that threaten the proper functioning of the networks. In this work, we propose a new intrusion detection model for Internet of Things, specifically for WSNs. This model relies on a geographic location check of the nodes to make sure that we communicate with the right node for each transaction. Subsequently, we proposed rules for detecting attacks. A mathematical model for trust calculating was proposed to update trust nodes values and eliminate malicious nodes. The simulation results were able to show the effectiveness of our model.

Hidden Markov Trust for Attenuation of Selfish and Malicious Nodes in the IoT Network

Research Square (Research Square), 2021

The exposure of IoT nodes to the internet makes them vulnerable to malicious attacks and failures. These failures affect the survivability, integrity, and connectivity of the network. Thus the detection and elimination of attacks in a timely manner become an important factor to maintain the network connectivity. Trust-based techniques are used in understanding the behavior of nodes in the network. Several researchers have proposed conventional trust models that are power-hungry and demand large storage space. Succeeding this Hidden Markov Models have also been developed to calculate trust but the survivability of network achieved from them is low. To improve the survivability selfish and malicious nodes present in the network are required to be treated separately. Hence, an improved Hidden Markov Trust (HMT) Model is developed in this paper which accurately detects the selfish and malicious nodes that illegally intercept the network. An algorithm is generalized for learning the behavior of nodes using the HMT model with the expected output. The evaluated node's likelihood functions differentiate the selfish node from the malicious node and provide independent timely treatment to both types of nodes. Further, comparative analysis for attacks such as black-hole, grey-hole, and sink-hole has been done and performance parameters have been extended to survivability-rate, power-consumption, delay, and false-alarm-rate, for different networks sizes and vulnerability. Simulation result provides a 10% higher PDR, 29% lower overhead, and 15% higher detection rate when compared to FUCEM, FTCSPM, and OADM trust models presented in the literature.

The Survey On Trust Management in Internet of Things

International Conference on Technological Solutions for Smart Economy, 2024

The future Internet of Things (IoT) system connects the physical world into cyberspace via billions of intelligent sensors and devices. The physical network's service-oriented architecture (SOA) establishes interoperability among different IoT devices. This connection between various devices has resulted in the heterogeneous nature of IoT networks. Thus, the IoT system imperatively calls for human-tomachine (H2M) and machine-to-machine (M2M) communication. This multi-billion connection of devices of the system architecture has raised some security and management challenges. Traditional security techniques like cryptography and authentication schemes may not remedy the situation because adversaries might subvert the network. Trust management in the Internet of Things system has become imperative because of its scalability, applicability, and robustness. Trust compositions dynamically estimate the information nodes provide to others in the network. The trust paradigm remains a promising approach to enhance service maximization in the IoTs network. The survey investigates the different trust evaluations, threat models, computational models, their lapses, and possible recommendations for effective system management.

A Novel Edge-Based Trust Management System for the Smart City Environment Using Eigenvector Analysis

Journal of Healthcare Engineering, 2022

e proposed Edge-based Trust Management System (E-TMS) uses an Eigenvector-based approach for eliminating the security threats present in the Internet of ings (IoT) enabled smart city environment. In most existing trust management systems, the trust aggregation process completely depends on the direct trust ratings obtained from both legitimate and malicious neighboring IoT devices. E-TMS possesses an edge-assisted two-level trust computation approach for ensuring the malicious free trust evaluation of IoTdevices. e E-TMS aims at removing the false contribution on aggregated trust data. It utilizes the properties of the Eigenvector for identifying compromised IoTdevices. e Eigenvector Analysis also helps to avoid false detection. e analysis involves a comparison of all the contributed trust data about every single connected device. A spectral matrix will be generated corresponding to the contributions and the received trust will be scaled based on the obtained spectral values. e absolute sum of obtained values will contain only true contributions. e accurate identi cation of false data will remove the e ect of malicious contributions from the nal trust value of a connected IoT device. Since the nal trust value calculated by the edge node contains only the trustworthy data, the prediction about the malicious nodes will be accurate. Eventually, the performance of E-TMS has been validated. roughput and network resilience are higher than the existing system.

Context-Aware Trust and Reputation Model for Fog-Based IoT

IEEE Access

Trust and reputation are important terms whether the communication is Humans-to-Human (H2H), Human-Machine-Interaction (HMI) or Machine-to-Machine (M2M). As Cloud computing and the internet of things (IoT) bring new innovations, they also cause various security and privacy issues. As numerous devices are continuously integrating as a core part of IoT, it is necessarily important to consider various security issues such as the trustworthiness of a user or detection of a malicious user. Moreover, fog computing also known as edge computing is revolutionizing the Cloud-based IoT by providing the Cloud services at the edge of the network, which can provide aid in overcoming security, privacy and trust issues. In this work, we propose a context-aware trust evaluation model to evaluate the trustworthiness of a user in a Fog based IoT (FIoT). The proposed approach uses a context-aware multi-source trust and reputation based evaluation system which helps in evaluating the trustworthiness of a user effectively. Further, we use context-aware feedback and feedback crawler system which helps in making trust evaluation unbiased, effective and reliable. Furthermore, we introduce monitor mode for malicious/untrustworthy users, which helps in monitoring the behavior and trustworthiness of a user. The proposed approach uses several tunable factors, which can be tuned based on the system's requirements. The simulations and results indicate that our approach is effective and reliable to evaluate the trustworthiness of a user.

Trust Management System in Internet of Things: A Survey

International Journal of Safety and Security Engineering

The Internet of Things (IoT) enables the connection of millions of disparate devices to the World Wide Web. Various smart devices must cooperate in order to complete the task. According to security experts, there are lot of risks related to IoT devices. Access control systems and protocols have faced a number of difficulties as a result of the development of the Internet of Things (IoT). The gadgets recognize other devices as part of their network service. Keeping participating devices safe is a crucial component of the Internet of Things. When gadgets communicate with one another, they require a promise of confidence. In order to increase security and usability of such modern technologies, trust between internet-connected devices and access control techniques is essential. Based on the facts presented, this article will help researchers create better access control techniques for the Internet of Things using trust based approach. The paper examines several access control and trust methods that could be applied in an IoT environment. An access control component is necessary to provide either access services to these recently connected devices or to those that have been joined to the IoT network for a long period. Scalable and dynamic trust computation is needed to provide dynamic access control. This review includes a thorough examination of trust management in a variety of situations and suggested design of trust computation model to provide access permission to IoT devices.