End-to-End Trust and Security for Internet of Things Applications

Bhattarai, Sulabh; Wang, Yong

doi:10.1109/mc.2018.2141038

Cited by 44 publications

(18 citation statements)

References 7 publications

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“…smartphone that operates between body sensors and the cloud) [256]. By implementing an edge computing framework, the ML/DL security model for the IoT system can be placed near the network edge, operate as guards to detect malicious behaviour, secure devices and reduce the severity of eavesdropping threats by approaching the IoT devices [257].…”

Section: Enabling Technology For Ml/dl Deployment For Iot Securitymentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

839

333

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The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT technologies play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT ecosystem effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. The ability to monitor IoT devices intelligently provides a significant solution to new or zero-day attacks. ML/DL are powerful methods of data exploration for learning about 'normal' and 'abnormal' behaviour according to how IoT components and devices perform within the IoT environment. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems.IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

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Section: Enabling Technology For Ml/dl Deployment For Iot Securitymentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

839

333

View full text Add to dashboard Cite

show abstract

“…When compared to other ICT technologies, the security risks associated with the wearables is higher because these are devices having limited computational power, battery life, and running a weaker encryption system in general. Considering, that these wearables collect a variety of information from the users via their sensors, the way security is implemented and its effect on the user perception is directly related to their trust in the system and the usage intention [32], [33], [89], [90]. The users will distrust the wearable devices in general if they are assumed to carry too high of a risk.…”

Section: Security Concerns and Trustmentioning

confidence: 99%

Antecedents of Trust and the Continuance Intention in IoT-Based Smart Products: The Case of Consumer Wearables

2019

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This work develops a theoretical model to examine the trust building process in an Internet of Things (IoT) based wearable platform. Specifically, various antecedents of trust are considered and its mediating role in the continuance usage intention of the wearables is studied. We also consider the moderating effect of personal innovativeness. An empirical survey is conducted, and 688 usable responses are obtained from the millennial generation users. The data is analyzed using the partial least squares method. Results show that privacy and security concerns are the two biggest hinderances to the trust building process. The users perceive mobility, and usability to be relative advantages, both of which play a positive role in enhancing the trust levels. The effect of personal innovativeness is also observed to be significant. Interestingly, the effect of social influence on the trust level is not supported, which means that boosting a positive image within the social community will not help in the diffusion and continuance usage of the wearables. Based upon the results suitable theoretical and practical implications are discussed, with stress being given to formulate specific strategies targeted towards the highly innovative users, who can improve the overall trust scenario and ensure a continued usage of the wearables.

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“…The recent work carried out by Alsaryrah et al [11] has used optimization scheme for controlling energy consumption of IoT nodes as well as upgrading the quality of service factors. Bhattarai and Wang [12] have discussed about significance of security over IoT system and presented an adversary model for highlighting the concerns associated with privacy. Bisadi et al [13] have presented a discussion about energy management system and presented architecture towards solving it.…”

Section: A the Backgroundmentioning

confidence: 99%

Robust Modelling to Jointly Address Security Threats, Bandwidth Utilization, and Energy issues in IoT

Ambore*,

2019

IJITEE

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The advent of Internet-of-Things (IoT) revolutionizes the mechanism of connectivity of massive chains of machine offering better improvement in data communication and analytical services. Review of existing approaches towards IoT performance improvement shows that there are still larger scope of enhancing the present solution. The proposed system introduces a mechanism where 3 different problems has been jointly addressed viz. security issues, channel capacity issues, energy depletion issues. The proposed model uses analytical research methodology where energy problem is offered common focus and linked with other two problem using stochastic method, probability logic, decision-making approach, packet scheduling, etc. The simulation outcome of the study shows that proposed system offers better energy efficiency, better resistivity against threats, and effective optimization of the bandwidth in contrast to existing related mechanism towards IoT ecosystem.

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End-to-End Trust and Security for Internet of Things Applications

Cited by 44 publications

References 7 publications

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Antecedents of Trust and the Continuance Intention in IoT-Based Smart Products: The Case of Consumer Wearables

Robust Modelling to Jointly Address Security Threats, Bandwidth Utilization, and Energy issues in IoT

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