Computer Networks 190(2021)107952 Contents lists available at ScienceDirect Computer Networks ELSEVIER journal homepage:www.elsevier.com/locate/comnet An adaptive trust model based on recommendation filtering algorithm for the Internet of Things systems Guozhu Chen",Fanping Zeng2,b,",Jian Zhangd,Tingting Lu",Jingfei Shen",Wenjuan Shua .School of Computer Science and Technology,University of Science and Technology of China,Hefei,Anhui,China Anhui Province Key Lab of Software in Computing and Communication,Hefei,Anhui,China State Key Laboratory of Computer Science,Institute of Software Chinese Academy of Sciences,Beijing,China University of Chinese Academy of Sciences,Beijing,China ARTICLE INFO ABSTRACT Keywords: The Internet of Things (loT)is growing rapidly and brings great convenience to humans.But it also causes Internet of Things some security issues which may have negative impacts on humans.Trust management is an effective method Trust model to solve these problems by establishing trust relationships among interconnected IoT objects.In this paper, we propose an adaptive trust model based on recommendation filtering algorithm for the IoT systems.The utilization of sliding window and time decay function when calculating direct trust can greatly accelerate the convergence rate of trust evaluation. We design a recommendation filtering algorithm to effectively filter out bad recommendations and minimize the impact of malicious objects.An adaptive weight is developed to better combine direct trust and recommendation trust into synthesis trust so as to adapt to the dynamically hostile environment.In the simulation experiments,we compare our adaptive trust model with three related models:TBSM,NRB and NTM. The experimental results indicate that our trust model converges fast and the mean absolute error is always less than 0.05 when the proportion of malicious nodes is from 10%to 70%.The comparative experiments further verify the effectiveness of our trust model in terms of accuracy,convergence rate and resistance to trust related attacks. 1.Introduction objects which have different functions and provide diverse services and applications.Consequently,an IoT trust model should be universal and The concept of Internet of Things (loT)is to connect a large number capable of running on various types of objects.Second,most objects of objects in the real physical world to the Internet based on standard have limited capacities so that the existing trust models in p2P and communication protocols and unique addressing schemes [1].These social networks are no longer applicable.Third,many of the objects will interconnected objects can be service providers offering services and sharing resources and information with each other.For the past few be malicious for their own benefits and then carry out various malicious years,IoT has grown rapidly and a series of relevant services and ap- attacks in order to reduce the trust value of others or improve their plications including smart home,smart city and smart community [2] own trustworthiness.As a result,IoT trust models should be resistant emerged.These services and applications bring great convenience to to those malicious attacks. humans,but they also cause some security issues that may do harm to To meet the challenges discussed above,we propose an adaptive our lives.For example,a misbehaved object can perform various types trust model to establish trust relationships among objects.Our trust of malicious attacks to destroy the integrity and availability of data model based on the recommendation filtering algorithm can effectively and network resources.Trust management is an effective method to resist malicious attacks carried out by misbehaved objects and evaluate solve the above security issues by establishing trust relationships among the trust value of target objects accurately.The major contributions of objects and then excluding malicious objects.It allows multiple objects to share their opinions about the trust value of their companions [3]. our paper are as follows: Although trust management can effectively solve some of the secu- rity problems,there are still some challenges in building trust man. We propose a system architecture based on trust third parties agement systems.First,there are a large number of heterogeneous (TTPs)which provides a secure and reliable trust computing Corresponding author at School of Computer Science and Technology,University of Science and Technology of China,Hefei,Anhui,China. E-mail addresses:chengz18@mail.ustc.edu.cn (G.Chen),billzeng@ustc.edu.cn (F.Zeng). https://doi.org/10.1016/j.comnet.2021.107952 Received 7 September 2020;Received in revised form 27 January 2021;Accepted 17 February 2021 Available online 22 February 2021 1389-1286/@2021 Elsevier B.V.All rights reserved.Computer Networks 190 (2021) 107952 Available online 22 February 2021 1389-1286/© 2021 Elsevier B.V. All rights reserved. Contents lists available at ScienceDirect Computer Networks journal homepage: www.elsevier.com/locate/comnet An adaptive trust model based on recommendation filtering algorithm for the Internet of Things systems Guozhu Chen a , Fanping Zeng a,b,∗ , Jian Zhang c,d , Tingting Lu a , Jingfei Shen a , Wenjuan Shu a a School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China b Anhui Province Key Lab of Software in Computing and Communication, Hefei, Anhui, China c State Key Laboratory of Computer Science, Institute of Software Chinese Academy of Sciences, Beijing, China d University of Chinese Academy of Sciences, Beijing, China A R T I C L E I N F O Keywords: Internet of Things Trust model A B S T R A C T The Internet of Things (IoT) is growing rapidly and brings great convenience to humans. But it also causes some security issues which may have negative impacts on humans. Trust management is an effective method to solve these problems by establishing trust relationships among interconnected IoT objects. In this paper, we propose an adaptive trust model based on recommendation filtering algorithm for the IoT systems. The utilization of sliding window and time decay function when calculating direct trust can greatly accelerate the convergence rate of trust evaluation. We design a recommendation filtering algorithm to effectively filter out bad recommendations and minimize the impact of malicious objects. An adaptive weight is developed to better combine direct trust and recommendation trust into synthesis trust so as to adapt to the dynamically hostile environment. In the simulation experiments, we compare our adaptive trust model with three related models: TBSM, NRB and NTM. The experimental results indicate that our trust model converges fast and the mean absolute error is always less than 0.05 when the proportion of malicious nodes is from 10% to 70%. The comparative experiments further verify the effectiveness of our trust model in terms of accuracy, convergence rate and resistance to trust related attacks. 1. Introduction The concept of Internet of Things (IoT) is to connect a large number of objects in the real physical world to the Internet based on standard communication protocols and unique addressing schemes [1]. These interconnected objects can be service providers offering services and sharing resources and information with each other. For the past few years, IoT has grown rapidly and a series of relevant services and ap￾plications including smart home, smart city and smart community [2] emerged. These services and applications bring great convenience to humans, but they also cause some security issues that may do harm to our lives. For example, a misbehaved object can perform various types of malicious attacks to destroy the integrity and availability of data and network resources. Trust management is an effective method to solve the above security issues by establishing trust relationships among objects and then excluding malicious objects. It allows multiple objects to share their opinions about the trust value of their companions [3]. Although trust management can effectively solve some of the secu￾rity problems, there are still some challenges in building trust man￾agement systems. First, there are a large number of heterogeneous ∗ Corresponding author at: School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China. E-mail addresses: chengz18@mail.ustc.edu.cn (G. Chen), billzeng@ustc.edu.cn (F. Zeng). objects which have different functions and provide diverse services and applications. Consequently, an IoT trust model should be universal and capable of running on various types of objects. Second, most objects have limited capacities so that the existing trust models in P2P and social networks are no longer applicable. Third, many of the objects will be malicious for their own benefits and then carry out various malicious attacks in order to reduce the trust value of others or improve their own trustworthiness. As a result, IoT trust models should be resistant to those malicious attacks. To meet the challenges discussed above, we propose an adaptive trust model to establish trust relationships among objects. Our trust model based on the recommendation filtering algorithm can effectively resist malicious attacks carried out by misbehaved objects and evaluate the trust value of target objects accurately. The major contributions of our paper are as follows: • We propose a system architecture based on trust third parties (TTPs) which provides a secure and reliable trust computing https://doi.org/10.1016/j.comnet.2021.107952 Received 7 September 2020; Received in revised form 27 January 2021; Accepted 17 February 2021