Trust management and evaluation for edge intelligence in the Internet of Things

Qureshi, Kashif Naseer; Iftikhar, Abeer; Bhatti, Shahid Nazeer; Piccialli, Francesco; Giampaolo, Fabio; Jeon, Gwanggil

doi:10.1016/j.engappai.2020.103756

Cited by 49 publications

(26 citation statements)

References 30 publications

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“…The recommendation-based trust evaluation provides a way to evaluate trust degree by requesting the surroundings nocentral authorise we have utilized an open-source library Zetta [58,65] to create central authorise and IoTivity library [23] to enable inter-object connectivity where the wireless communication is performed by utilizing Zigbee (IEEE 802.15) [28]. Several existing mechanism has been utilized to compare the performance which are TMEI [49], RobustD [26], SGSQ-TM [19].…”

Section: Recommendation-based Indirect Trustmentioning

confidence: 99%

EdgeTrust - A Lightweight Data-centric Trust Management Approach for Green Internet of Edge Things

Awan

Din

Almogren

et al. 2021

Preprint

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Internet of Things (IoT) is bringing revolution into today’s world where devices in our surroundings become smart and perform daily-life activities and operations with more precision. The architecture of IoT is heterogeneous as it provides autonomy to nodes that they can communicate among other nodes and can also exchange information at any period. Due to the heterogeneous environment, IoT faces numerous security and privacy challenges, and one of the most significant challenges is the identification of malicious and compromised nodes. In this article, we have proposed a Machine Learning-based trust management approach for edge nodes. The proposed approach is a lightweight process to evaluate trust because edge nodes cannot perform complex computations. To evaluate trust, the proposed mechanism utilizes the knowledge and experience component of trust where knowledge is further based on several parameters. To eliminate the triumphant execution of good and bad-mouthing attacks, the proposed approach utilizes edge clouds, i.e., local data centers, to collect recommendations to evaluate indirect and aggregated trust. The trustworthiness of nodes is ranked between a certain limit where only those that satisfy the threshold value can participate in the network. To validate the performance of a proposed approach we have performed an extensive simulation in comparison with the existing approaches and the result shows the effectiveness of the proposed approach against several potential attacks.

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Section: Recommendation-based Indirect Trustmentioning

confidence: 99%

EdgeTrust - A Lightweight Data-centric Trust Management Approach for Green Internet of Edge Things

Awan

Din

Almogren

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Moreover, the home automation system requires the sensor devices to be connected to the cloud and are usually controlled from the user's mobile. Furthermore, real-time IoT networks provide many benefits and also suffer from various security vulnerabilities, such as data leakage, multi-latency, side-channels, and cross-site scripting [1][2][3]. An IoT sensor network collects data from a source node and passes it to the multiple intermediate sensor nodes to reach a destination node.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Bargain Game Theory in the Internet of Things for Data Trustworthiness

Sumathi

Muthuramalingam

Prado

et al. 2021

Sensors

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Smart home and smart building systems based on the Internet of Things (IoT) in smart cities currently suffer from security issues. In particular, data trustworthiness and efficiency are two major concerns in Internet of Things (IoT)-based Wireless Sensor Networks (WSN). Various approaches, such as routing methods, intrusion detection, and path selection, have been applied to improve the security and efficiency of real-time networks. Path selection and malicious node discovery provide better solutions in terms of security and efficiency. This study proposed the Dynamic Bargaining Game (DBG) method for node selection and data transfer, to increase the data trustworthiness and efficiency. The data trustworthiness and efficiency are considered in the Pareto optimal solution to select the node, and the bargaining method assigns the disagreement measure to the nodes to eliminate the malicious nodes from the node selection. The DBG method performs the search process in a distributed manner that helps to find an effective solution for the dynamic networks. In this study, the data trustworthiness was measured based on the node used for data transmission and throughput was measured to analyze the efficiency. An SF attack was simulated in the network and the packet delivery ratio was measured to test the resilience of the DBG and existing methods. The results of the packet delivery ratio showed that the DBG method has higher resilience than the existing methods in a dynamic network. Moreover, for 100 nodes, the DBG method has higher data trustworthiness of 98% and throughput of 398 Mbps, whereas the existing fuzzy cross entropy method has data trustworthiness of 94% and a throughput of 334 Mbps.

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“…In order to avoid networks being attacked by malicious nodes, researchers have recently carried out related studies. In [23], Qureshi et al proposed an effective technique based on cumulative trust evaluation. In [24], Raza et al proposed a novel scheme in which each node can identify the malicious node and select the route by calculating the trust level of its neighboring nodes.…”

Section: Introductionmentioning

confidence: 99%

Model and Analyze the Cascading Failure of Scale-Free Network Considering the Selective Forwarding Attack

et al. 2021

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Many real-world networks have scale-free characteristics and can be abstracted into scalefree networks. Aiming at the problem that scale-free networks have low fault tolerance in the face of malicious attacks, we focus on the selective forwarding attack behavior that exists widely in the networks, and build a selective forwarding attack model based on node importance. Moreover, according to the neighbor node's malicious and non-malicious behavior, a load redistribution strategy for failed node is proposed. Then, the network's damage degree is given to evaluate the comprehensive impact of cascading failure phenomenon on network connectivity and information integrity under selective forwarding attack. Finally, a cascading failure model of scale-free networks considering selective forwarding attack behavior is obtained. Based on this model, the propagation condition without triggering network cascading failure, the selective forwarding attack intensity threshold and the load loss ratio are obtained. By simulation on the classical BA scale-free network model, the results show that multiple nodes' random failure occurs in scalefree networks, selective forwarding attack behavior is helpful to improve the connectivity of the network. Besides, the network exists selective forwarding attack intensity threshold, when attack intensity is greater than the intensity threshold, the malicious nodes will not fail because of the failure of the neighbor nodes. But, selective forwarding attack behavior can destroy the information integrity, and there is a negative correlation between the attack intensity and the information integrity. These results have certain guiding significance for cascade failure analysis and prevention method research and design in real life. INDEX TERMS Scale-free network, cascading failure, selective forwarding attack, attack intensity threshold, information integrity This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Trust management and evaluation for edge intelligence in the Internet of Things

Cited by 49 publications

References 30 publications

EdgeTrust - A Lightweight Data-centric Trust Management Approach for Green Internet of Edge Things

EdgeTrust - A Lightweight Data-centric Trust Management Approach for Green Internet of Edge Things

Dynamic Bargain Game Theory in the Internet of Things for Data Trustworthiness

Model and Analyze the Cascading Failure of Scale-Free Network Considering the Selective Forwarding Attack

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