Abstract
Recently, Internet of Things (IoT) technology has emerged in many aspects of life, such as transportation, healthcare, and even education. IoT technology incorporates several tasks to achieve the goals for which it was developed through smart services. These services are intelligent activities that allow devices to interact with the physical world to provide suitable services to users anytime and anywhere. However, the remarkable advancement of this technology has increased the number and the mechanisms of attacks. Attackers often take advantage of the IoTs’ heterogeneity to cause trust problems and manipulate the behavior to delude devices’ reliability and the service provided through it. Consequently, trust is one of the security challenges that threatens IoT smart services. Trust management techniques have been widely used to identify untrusted behavior and isolate untrusted objects over the past few years. However, these techniques still have many limitations like ineffectiveness when dealing with a large amount of data and continuously changing behaviors. Therefore, this paper proposes a model for trust management in IoT devices and services based on the simple multi-attribute rating technique (SMART) and long short-term memory (LSTM) algorithm. The SMART is used for calculating the trust value, while LSTM is used for identifying changes in the behavior based on the trust threshold. The effectiveness of the proposed model is evaluated using accuracy, loss rate, precision, recall, and F-measure on different data samples with different sizes. Comparisons with existing deep learning and machine learning models show superior performance with a different number of iterations. With 100 iterations, the proposed model achieved 99.87% and 99.76% of accuracy and F-measure, respectively.
Highlights
The rationale behind the Internet of Things (IoT) paradigm was proposed way back in the 1980s with the idea of ubiquitous computing [1]
In the misbehaving detection sub-stage, the long short-term memory (LSTM) technique is used for classification/prediction tasks, which is known as an excellent technique for identifying changes in behavior
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Summary
The rationale behind the Internet of Things (IoT) paradigm was proposed way back in the 1980s with the idea of ubiquitous computing [1]. Such so lutionicsnomhqaupvaonneteibnfyetsien,nagnuudsndecedearlttiaoningotwypitftiohmrIoiuzTne’tsrpudsrytonetademcbitecihonanavt,iuosruresp,anpcdhoohrotestdienregocgiteshnieoeniotpy-.mtimaaklintrguspt rmoocedsesl es, iden tify untruAstimededbaeththaevifoorrm, eisroislasutees,utnhitsrupaspteedr porobpjeocstess,aatnrudstrmedaniraegcetmfeunnt mctoiodnelafloitryIotTo truste zonesd[e1v0ic]e. SVaanrdiosuersviacpesptrhoaatcthakeess, lseuvcehragaes f[r1o1m–1m4u],lthi-carviteerbiaeednecidsieovne-mloapkeindgbayndredseeeaprchers a solutions to trust issues These solutions are still unable to fully address tru issues and face numerous challenges, such as a lack of effectiveness when dealing wit large amounts of data and constantly changing behaviors, high energy utilization, diff culty in quantifying uncertainty for untrusted behaviors, choosing the optimal tru Sensors 2022, 22, 634 Sensors 2022, 22, x FOR PEER REVIEW. TThhiiss rreeffeerrss ttoo tthhee ccoommppoonneennttss tthhaatt aarree ccoonnssiiddeerreedd iinn ttrruusstt ccoommppuuttaattiioonn aanndd iitt iinnvvoollvveess ttwwoo mmaajjoorr mmoodduulleess nnaammeellyy:: qquuaalliittyy ooff sseerrvviiccee ((QQooSS)) ttrruusstt aanndd ssoocciiaall ttrruusstt [[88]]. SSoocciiaallrreellaattiioonnsshhiipp ttrruussttiissuusseeddtotoasassessesssthteheIoIToTenetinttyittyo teovaelvuaaltueawtehwethheetrhietrisittriussttrwuostrwthoyrtohrynoort. nBoet-. sBideseisd, esso,csiaolctiraul sttruusttiliuzteisliztreussttrpursot pperrotpieesr,tsieusc,hsuaschhoansehsotyn,ecsetny,trcaelnittyr,ailnittyi,minactiym, pacriy-, vparcivya, cayn,dancdoncnoencntievcittiyv,ittyo, mtoemaseuarseutrreutsrtuvstalvuaelsue[8s][.8]
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