Context-oriented trust computation model for industrial Internet of Things☆
Introduction
Industry 4.0 is the most significant industrial revolution, which is focused on creating smart factories by using smart machines [1]. Generally, Industry 4.0 is used for the automation and exchange of data between smart machines for manufacturing purposes, which comprise the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Cyber–Physical Systems (CPS). The IoT connects physical objects through the Internet using sensors, RFID tags, and various smart devices. Sensing devices are used to get the stimulus from the environment and respond to the system. IoT has become a fundamental part of the smart environments and provides multiple services in safety, transport, healthcare, surveillance systems, education, and more importantly, in the industrial domain. The communication in IIoT is based on IoT-enabled devices that can run numerous applications for collaborative communication in smart manufacturing to generate large amounts of data. This data needs to be trustworthy and secure by isolating false data generated by the malicious nodes. This can be achieved through edge intelligence, which refers to the process of data collection, analysis, and related calculations at the node that captures or generates the data. IIoT is providing solutions to smart manufacturing in combination with security mechanisms to ensure the reliability of data and to improve communication between smart machines. Although this makes IoT helpful in everyday life, it also opens the doors of threats and vulnerabilities [2]. For the service exchange plan, devices need to be in contact with each other to share the data to provide services in industrial applications. There should be mutual trust between the service requester and the service provider. Therefore, it is imperative to measure the reliability of service providers.
Trust formation is important and can have different definitions according to the requirement. Trust is defined as the only requirement that is used to access the resources and information that need to be shared. The best definition of trust found in the IoT literature is, “Trust is the edge that links the intelligent object with the technological ecosystem” [3]. The presence of trust is helpful in making decisions in the network that comprises multiple systems, which makes it a significant part of the system for devices to perform the requested services. In an IoT network, trust is established among different nodes to complete the requested task. The important factor is when they need to associate with the unknown devices. At that time, devices need to have some trust establishment procedure to communicate with unfamiliar ones. IoT is a blend of various types of devices in the same network; thus, trustworthy communication between all associated entities is important [3].
This research proposes an intelligent trust model, which helps the edge nodes to request services from a reliable service provider instead of a malicious one. Since data is the most significant part of an IIoT architecture, we must consider the trust among entities to share the data for any given task. It is important to consider the context of a service provider in a network for trust calculation. The nodes that are used in the trust calculation process belong to various contextual environments; therefore, the Trust Management System (TMS) must include the context-similar nodes only while formulating the trust. A context in our research is to consider multiple factors, as discussed in Section 3, while taking the recommendations from other nodes. Context represents the environment of users and servers under which they interact to establish a service interaction. This represents the current user environment, server location, and Quality of Service (QoS) parameters.
The main contributions of this research are as follows:
- 1.
We propose a context-based adaptive IoT trust model for edge intelligence. The novelty is in using collaborative filtering to collect trust feedback from nodes that get services under the same context.
- 2.
We develop a direct trust calculation mechanism using actual user satisfaction experience, selected between the range (0–1), based on the percentage of positive observations instead of binary representation, which was previously used in the literature.
- 3.
We develop an adaptive calculation mechanism to allocate weights to direct trust and indirect observations based upon the current user experience while considering the context.
- 4.
The proposed Context-based Trust Evaluation System (CTES) model is designed and verified using High-Level Petri Nets (HLPN).
- 5.
We evaluate CTES to analyze its effectiveness in avoiding malicious nodes against ballot stuffing and bad-mouthing attacks.
The rest of the paper is organized as follows: The introduction section is followed by Section 2, which presents the related work in the domain of IoT. Section 3 describes the proposed CTES model, which gives a clear explanation and working of the mentioned techniques for trust calculation. Section 4 presents the formal modeling and verification of the proposed CTES model. Section 5 provides the performance evaluation of CTES on IoT edge nodes in terms of their accuracy and dynamic assignment of weights to calculate the trust score. It also demonstrates the effectiveness of malicious node avoidance using CTES in the presence of ballot stuffing and bad-mouthing attacks. Finally, Section 6 concludes the paper with our findings and future directions.
Section snippets
Related work
IIoT is currently playing a significant role in smart manufacturing for Industry 4.0. CPS, IoT, and IIoT are collectively working for data generation and to analyze the collected data. Cognitive capabilities for the collected data for smart manufacturing are proposed in [4]. Multiple reference architectures that help for smart manufacturing by using CPS and IIoT are discussed in [5] along with their pros and cons. An architectural classification for Industry 4.0 is proposed to divide it into
Context-based trust evaluation system model
The proposed Context-Based Trust Evaluation System (CTES) is a distributed trust management system. Each user calculates its trust score, which depends upon the context of the requested service from the service provider. In order to achieve scalability, each user calculates the trust score towards a limited set of requested services to which it interacted. Each user stores this information in the form of lists. The information list of user includes the following:
- 1.
A list of server IDs denoted
Formal modeling and verification of context-based trust evaluation system (CTES)
This section presents the verification and formal modeling of the proposed CTES model for an IoT environment. The main purpose of using formal modeling and verification is to verify the working process of the CTES based algorithms through mathematical rules. The High-level Petri Nets (HLPN) are used to display the formal modeling and verification of CTES calculations. HLPN is defined as a 7-tuple in the form of , where is the set of places, is the set of transitions, and
Performance analysis of CTES
To evaluate and validate our CTES model, we use IoT Sentinel dataset, which represents the traffic of IoT-based smart nodes of different types. It considers a network of smart devices of different types. The setup of IoT-based smart node is repeated times for each device type to get better accuracy. The dataset directories contain several pcap files, and each pcap file represents the setup of the given scenario. We have used the parameters of existing servers, in addition to their
Conclusion and future work
In this paper, we have proposed and analyzed a distributed CTES model for trust calculation in smart objects, which allows the requesting object to trust the service provider in an IIoT environment. The direct observations of edge nodes (trustor) are represented between a range of , which creates the flexibility between trustor and trustee to highlight actual observations. Since CTES is a context-based model, we have calculated the similarity index of existing context among neighboring nodes
CRediT authorship contribution statement
Ayesha Altaf: Conceive and designed the analysis, Methodology, Software (Matlab), Contributed data, Analysis tools, Data curation, Writing and documentation. Haider Abbas: Conceive and designed the analysis, Contributed data, Investigation of methodology and results, Data curation, Writing - review & editing. Faiza Iqbal: Methodology, Investigation of methodology and results, Writing and documentation, Writing - review & editing. Farrukh Aslam Khan: Contributed data, Investigation of
Acknowledgments
The authors extend their sincere appreciation to the Deanship of Scientific Research at King Saud University, Saudi Arabia, for funding this work through the Research Group No. RGP-214. This research is also supported by the Higher Education Commission (HEC) , Pakistan through its initiative of National Center for Cyber Security for the affiliated lab National Cyber Security Auditing and Evaluation Lab (NCSAEL), Grant No: 2(1078)/HEC/M&E/2018/707.
Ayesha Altaf did her B.S. in Computer Science from COMSATS Lahore in 2006 and M.S. Degree in Information Security from National University of Sciences and Technology (NUST), Pakistan in 2009. She is continuing her PhD Degree in Information Security from NUST, Pakistan. Her research interests include Internet of Things security, Trust Modeling and Information Security.
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Ayesha Altaf did her B.S. in Computer Science from COMSATS Lahore in 2006 and M.S. Degree in Information Security from National University of Sciences and Technology (NUST), Pakistan in 2009. She is continuing her PhD Degree in Information Security from NUST, Pakistan. Her research interests include Internet of Things security, Trust Modeling and Information Security.
Haider Abbas is currently heading the National Cyber Security Auditing and Evaluation Lab (NCSAEL) at MCS NUST. He is a Cyber Security professional, researcher and industry consultant who took professional trainings and certifications from (MIT), USA, Sweden, IBM, USA and EC-Council. He received his MS in Engineering and MIS (2006) and PhD in Information Security (2010) from KTH-Stockholm, Sweden. He is also an adjunct faculty and doctoral studies advisor at Florida Institute of Technology, USA and Manchester Metropolitan University, United Kingdom.
Faiza Iqbal received her M.S. and Ph.D. degrees in software engineering from NUST, Pakistan, in 2009 and 2015 respectively. She has been associated with Quaid-i-Azam University, Islamabad as Assistant Professor. Currently, she is working as Assistant Professor at The University of Lahore (UoL), Lahore. Her current research interests are knowledge based systems, network optimization modeling, and high performance protocol design.
Farrukh Aslam Khan is currently working as a Professor at the Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh, Saudi Arabia. He has over 20 years of teaching and research experience at various institutions. His research interests include Cyber Security, Wireless Sensor Networks and e-Health, Smart Grid, Bio-inspired and Evolutionary Computation, and the Internet of Things.
Saddaf Rubab is an experienced researcher with a demonstrated history of working in the higher education industry. Skilled in Data Science, Artificial Intelligence, Cognitive Science, Grid Computing, and Operations Research. Strong research professional with a Doctor of Philosophy (Ph.D.) focused in Information Technology from Universiti Teknologi PETRONAS, Malaysia.
Abdelouahid Derhab received the Engineer, Master, and Ph.D. degrees in computer science from University of Sciences and Technology Houari Boummediene (USTHB), Algiers, in 2001, 2003, and 2007 respectively. His research interests include: Mobile Adhoc Networks (MANETs), Wireless Sensor Networks (WSNs), Intrusion detection systems, Botnet detection, and Mobile security.
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This paper is for special section VSI-eiia. Reviews processed and recommended for publication by Guest Editor Dr. Jiafu Wan.