scholarly journals Construction of Internet of things trusted group based on multidimensional attribute trust model

2021 ◽  
Vol 17 (1) ◽  
pp. 155014772198988
Author(s):  
Jinghan Chen ◽  
Bei Gong ◽  
Yubo Wang ◽  
Yu Zhang
Keyword(s):  
Internet Of Things ◽  
Trust Model ◽  
Group Signature ◽  
Malicious Nodes ◽  
Maintenance Mechanism ◽  
Proposed Model ◽  
Dynamic Maintenance ◽  
Trust Metric ◽  
Network Overhead ◽  
Trust Measurement

Accurate prediction of the trust relationship is the basis for trusted access and secure interaction between Internet of things nodes. To evaluate the degree of trust, a trust metric is assigned to every node depending on its several attributes. Normal nodes in Internet of things tend to suffer collusion attacks from malicious nodes; thus, the accuracy of the trust measurement decreases. To enhance the security of interaction between massive Internet of things nodes, we propose a multidimensional attribute trust model and a dynamic maintenance mechanism of a trusted group. The proposed model provides a reference for the selection and evaluation of node multidimensional attribute factors to adapt to different Internet of things application scenarios. The dispersion of satisfaction records is used to discover abnormal data and weaken its influence on the calculation of the node’s comprehensive trust evaluation. The construction of trusted groups provides an architectural foundation for the application of group signature that maintains low network overhead. The performance of multidimensional attribute trust model and dynamic maintenance mechanism is verified using Netlogo. Simulation results show the efficiency of the proposed model to classify the malicious nodes and honest nodes, as well as to build a trusted group that could ensure honest nodes occupy the major proportion.

Study on Trust Model of Internetware System

2015 ◽  
Vol 713-715 ◽  
pp. 2486-2490
Author(s):  
Tao He ◽  
Yong Wei ◽  
Hua Zhong Li ◽  
Li Na Fang ◽  
Shou Xiang Xu ◽  
...  
Keyword(s):  
Time Frame ◽  
Trust Model ◽  
Game Model ◽  
Measurement Results ◽  
Dynamic Trust ◽  
On Line ◽  
Weighted Factor ◽  
Trust Metric ◽  
Trust Measurement ◽  
Metric Model

We take the overall architecture of internetware on-line evolution model as basic, and study on trust metric model of the software in internetware system. In view of the not accurate results from the rough and existing trust metric model granularity, this paper proposed a multi service and hierarchical dynamic trust metric model based on time frame. Model also offer a method to established time frame weighted factor based on inducing ordered weighted operator, which makes the trust measurement results more accurate. The trust measurement results obtained from the model will be used as decision-making basis for Bias game model.

scholarly journals Comparative Analysis of Trust Establishment Model to Identify Malicious Nodes in SIoT

2021 ◽  
Vol 10 (02) ◽  
pp. 47-55
Author(s):  
Anciline JeniferJ ◽  
Piramu PreethikaS K
Keyword(s):  
Internet Of Things ◽  
Evaluation System ◽  
Trust Model ◽  
Packet Transmission ◽  
Malicious Nodes ◽  
Trust Value ◽  
Trust Establishment ◽  
The Social ◽  
Social Internet Of Things ◽  
Security And Reliability

Internet of Things enables the user to interact with devices which merges with Social Internet of Things (SIoT). SIoT is a new model that allows various attractive application and promote sharing of information. This can establish objects in an independent way based on the social relationship. The major issue is how to construct the trusted model and to understand how the objects interact with SIoT. In order to overcome these challenges, trust establishment model among these devices has been required before originating communication. This paper describes collaborative methods for calculating trust based on the trust evaluation system. The collaboration among the nodes can be established using encoded and decoded packets whereas the encoded packet transmission illustrates the collaboration. The each node of reliability based on the transaction factors can be assigned and their trust values can be calculated. This paper described comparison between proposed Cooperative Trust (CT) models which can be observed initially it achieves 79% trust value than the existing trust model. This framework provides more security and reliability for SIoT in order to identify the malicious nodes.

scholarly journals Multi-Layer Blockchain-Based Security Architecture for Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 772 ◽  
Author(s):  
Houshyar Honar Pajooh ◽  
Mohammad Rashid ◽  
Fakhrul Alam ◽  
Serge Demidenko
Keyword(s):  
Internet Of Things ◽  
Clustering Algorithm ◽  
Model Development ◽  
Base Stations ◽  
Smart Devices ◽  
Security Model ◽  
Cluster Heads ◽  
Authentication Mechanism ◽  
Proposed Model ◽  
Authentication And Authorization

The proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the security problems within the 5G-enabled IoT networks. This paper proposes a Multi layer Blockchain Security model to protect IoT networks while simplifying the implementation. The concept of clustering is utilized in order to facilitate the multi-layer architecture. The K-unknown clusters are defined within the IoT network by applying techniques that utillize a hybrid Evolutionary Computation Algorithm while using Simulated Annealing and Genetic Algorithms. The chosen cluster heads are responsible for local authentication and authorization. Local private blockchain implementation facilitates communications between the cluster heads and relevant base stations. Such a blockchain enhances credibility assurance and security while also providing a network authentication mechanism. The open-source Hyperledger Fabric Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The simulation results demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported approaches. The proposed lightweight blockchain model is also shown to be better suited to balance network latency and throughput as compared to a traditional global blockchain.

scholarly journals Research on Consumer Initial Trust Model Based on Internet of Things

2021 ◽  
Vol 1769 (1) ◽  
pp. 012006
Author(s):  
Ai-Ling Wang ◽  
Lei-ming Li ◽  
Guo-ling Xu
Keyword(s):  
Internet Of Things ◽  
Trust Model ◽  
Initial Trust ◽  
Model Based

Proposed Back Propagation Deep Neural Network for Intrusion Detection in Internet of Things Fog Computing

2021 ◽  
Vol 9 (4) ◽  
pp. 464-469
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Fog Computing ◽  
Denial Of Service ◽  
Back Propagation ◽  
Security And Privacy ◽  
Brute Force ◽  
Iot Platform ◽  
Proposed Model

Internet of things (IoT) is an emerging concept which aims to connect billions of devices with each other anytime regardless of their location. Sadly, these IoT devices do not have enough computing resources to process huge amount of data. Therefore, Cloud computing is relied on to provide these resources. However, cloud computing based architecture fails in applications that demand very low and predictable latency, therefore the need for fog computing which is a new paradigm that is regarded as an extension of cloud computing to provide services between end users and the cloud user. Unfortunately, Fog-IoT is confronted with various security and privacy risks and prone to several cyberattacks which is a serious challenge. The purpose of this work is to present security and privacy threats towards Fog-IoT platform and discuss the security and privacy requirements in fog computing. We then proceed to propose an Intrusion Detection System (IDS) model using Standard Deep Neural Network's Back Propagation algorithm (BPDNN) to mitigate intrusions that attack Fog-IoT platform. The experimental Dataset for the proposed model is obtained from the Canadian Institute for Cybersecurity 2017 Dataset. Each instance of the attack in the dataset is separated into separate files, which are DoS (Denial of Service), DDoS (Distributed Denial of Service), Web Attack, Brute Force FTP, Brute Force SSH, Heartbleed, Infiltration and Botnet (Bot Network) Attack. The proposed model is trained using a 3-layer BP-DNN

scholarly journals A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

2021 ◽  
pp. 182-189
Author(s):  
S. Arokiaraj ◽  
Dr. N. Viswanathan
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Short Term Memory ◽  
The Other ◽  
Learning Models ◽  
Computational Overhead ◽  
Temporal Features ◽  
Human Movements ◽  
Proposed Model ◽  
Iot Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

scholarly journals A Weakly Supervised Academic Search Model Based on Knowledge-Enhanced Feature Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mingying Xu ◽  
Junping Du ◽  
Feifei Kou ◽  
Meiyu Liang ◽  
Xin Xu ◽  
...  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Feature Representation ◽  
Search Model ◽  
Model Based ◽  
Proposed Model ◽  
Cloud Server ◽  
Weakly Supervised ◽  
Internet Of Things Technology ◽  
Academic Search

Internet of Things search has great potential applications with the rapid development of Internet of Things technology. Combining Internet of Things technology and academic search to build academic search framework based on Internet of Things is an effective solution to realize massive academic resource search. Recently, the academic big data has been characterized by a large number of types and spanning many fields. The traditional web search technology is no longer suitable for the search environment of academic big data. Thus, this paper designs academic search framework based on Internet of Things Technology. In order to alleviate the pressure of the cloud server processing massive academic big data, the edge server is introduced to clean and remove the redundancy of the data to form a clean data for further analysis and processing by the cloud server. Edge computing network effectively makes up for the deficiency of cloud computing in the conditions of distributed and high concurrent access, reduces long-distance data transmission, and improves the quality of network user experience. For Academic Search, this paper proposes a novel weakly supervised academic search model based on knowledge-enhanced feature representation. The proposed model can relieve high cost of acquisition of manually labeled data by obtaining a lot of pseudolabeled data and consider word-level interactive matching and sentence-level semantic matching for more accurate matching in the process of academic search. The experimental result on academic datasets demonstrate that the performance of the proposed model is much better than that of the existing methods.

scholarly journals A Framework to Systematically Analyse the Trustworthiness of Nodes for Securing IoV Interactions

2020 ◽  
Vol 21 (3) ◽  
pp. 451-462
Author(s):  
Indu Bhardwaj ◽  
Sibaram Khara ◽  
Priestly Shan
Keyword(s):  
Standard Deviation ◽  
Essential Role ◽  
Trust Model ◽  
Review Literature ◽  
Malicious Node ◽  
Threshold Values ◽  
Malicious Nodes

Trust plays essential role in any securing communications between Vehicles in IOV. This motivated us to design a trust model for IoV communication. In this paper, we initially review literature on IoV and Trust and present a hybrid trust model that separates the malicious and trusted nodes to secure the interaction of vehicle in IOV. Node segregation is done using value of statistics (St). If St of each node lies in the range of mean (m) plus/minus 2 standard deviation (SD) of PDR then nodes behaviour is considered as normal otherwise malicious. The simulation is conducted for different threshold values. Result depicts that PDR of trusted node is 0.63 that is much higher than the PDR of malicious node that is 0.15. Similarly, the average no. of hops and trust dynamics of trusted nodes are higher than that of malicious node. So, on the basis of values of PDR, number of available hops and trust dynamics, the malicious nodes can be clearly identified and discarded.

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