A security evaluation of popular Internet of Things protocols for manufacturers

Celebucki, Daniel; Lin, Maj Alan; Graham, Steve

doi:10.1109/icce.2018.8326099

Cited by 19 publications

(9 citation statements)

References 9 publications

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“…The work in [90] focuses on the ZigBee, Z-wave, and BLE protocols, and details on their security aspects and deficiencies as depicted by the relevant literature. In the same year, the authors of [91] elaborated on the security features of Z-Wave and Thread protocols in an effort to identify present and future security challenges.…”

Section: Group IImentioning

confidence: 99%

A State-of-the-Art Review on the Security of Mainstream IoT Wireless PAN Protocol Stacks

Kambourakis

Kolias²,

Geneiatakis³

et al. 2020

Symmetry

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Protocol stacks specifically designed for the Internet of Things (IoT) have become commonplace. At the same time, security and privacy concerns regarding IoT technologies are also attracting significant attention given the risks that are inherently associated with the respective devices and their numerous applications, ranging from healthcare, smart homes, and cities, to intelligent transportation systems and industrial automation. Considering the still heterogeneous nature of the majority of IoT protocols, a major concern is to find common references for investigating and analyzing their security and privacy threats. To this end, and on top of the current literature, this work provides a comprehensive, vis-à-vis comparison of the security aspects of the thus far most widespread IoT Wireless Personal Area Network (WPAN) protocols, namely BLE, Z-Wave, ZigBee, Thread, and EnOcean. A succinct but exhaustive review of the relevant literature from 2013 up to now is offered as a side contribution.

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Section: Group IImentioning

confidence: 99%

A State-of-the-Art Review on the Security of Mainstream IoT Wireless PAN Protocol Stacks

Kambourakis

Kolias²,

Geneiatakis³

et al. 2020

Symmetry

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“…One may cite for example Dinu and Kizhvatov [13] on Thread and Rouch et al [14] regarding Z-Wave. The consensus on these protocols is that security is acceptable in most cases [15] because most security breaches are coming from implementation issues.…”

Section: Related Workmentioning

confidence: 99%

An Enrolment Gateway for Data Security in Heterogeneous Industrial Internet of Things

Martin-Tricot

Eichler

Berthomé

2020

2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)

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Industry includes more and more IoT components to have a better control on production and logistic processes.Unfortunately, growing network openness in a formerly isolated world induces major security risks which are especially critical in an industrial context. These risks are exacerbated by the highly fragmented nature of the industrial IoT market which imposes interoperability management and challenges security.We propose an approach to enable end-to-end data security within a heterogeneous IoT deployment. Interoperability is ensured by a central network powered by oneM2M interacting with various tier protocols. In this paper, we focus on secure communication with ZigBee and discuss how it can be transposed to other protocols, namely Z-Wave and Thread.

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“…Connecting a large number of devices to the Internet and allowing them to actively engage in the network, IoT allows machine-to-machine communication over the Internet [2]. In 2017, there were roughly 27 billion IoT devices, and by 2020, this number is anticipated to reach 50 billion [3]. There are a lot of security issues with IoT settings since they are generally made up of a lot of heterogeneous and low-cost devices that have little or no security built-in [4].…”

Section: Introductionmentioning

confidence: 99%

Intelligent system for IoT botnet detection using SVM and PSO optimization

Salam¹

2021

JISIoT

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Botnet attacks involving Internet-of-Things (IoT) devices have skyrocketed in recent years due to the proliferation of internet IoT devices that can be readily infiltrated. The botnet is a common threat, exploiting the absence of basic IoT security technologies and can perform several DDoS attacks. Existing IoT botnet detection methods still have issues, such as relying on labeled data, not being validated with newer botnets, and using very complex machine learning algorithms, making the development of new methods to detect compromised IoT devices urgent to reduce the negative implications of these IoT botnets. Due to the vast amount of normal data accessible, anomaly detection algorithms seem to promise for identifying botnet attacks on the Internet of Things (IoT). For anomaly detection, the One-Class Support vector machine is a strong method (ONE-SVM). Many aspects influence the classification outcomes of the ONE-SVM technique, like that of the subset of features utilized for training the ONE-SVM model, hyperparameters of the kernel. An evolutionary IoT botnet detection algorithm is described in this paper. Particle Swarm Optimization technique (PSO) is used to tune the hyperparameters of the ONE-SVM to detect IoT botnet assaults launched from hacked IoT devices. A new version of a real benchmark dataset is used to evaluate the proposed method's performance using traditional anomaly detection evaluation measures. This technique exceeds all existing algorithms in terms of false positive, true positive and rates, and G-mean for all IoT device categories, according to testing results. It also achieves the shortest detection time despite lowering the number of picked features by a significant amount.

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A security evaluation of popular Internet of Things protocols for manufacturers

Cited by 19 publications

References 9 publications

A State-of-the-Art Review on the Security of Mainstream IoT Wireless PAN Protocol Stacks

A State-of-the-Art Review on the Security of Mainstream IoT Wireless PAN Protocol Stacks

An Enrolment Gateway for Data Security in Heterogeneous Industrial Internet of Things

Intelligent system for IoT botnet detection using SVM and PSO optimization

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