IoT-Flock: An Open-source Framework for IoT Traffic Generation

Ghazanfar, Syed; Hussain, Faisal; Rehman, Atiq Ur; Fayyaz, Ubaid U.; Shahzad, Farrukh; Shah, Ghalib A.

doi:10.1109/icetst49965.2020.9080732

Cited by 51 publications

(49 citation statements)

References 9 publications

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“…The IoT devices are resource constraint because of the limited amount of memory, computational capacity, and power [ 16 , 17 ]. The traditional security solutions are not adequate for efficiently detecting the malicious attacks in IoT environment due to the distinct features of IoT devices [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…The traditional security mechanisms are deployed at two levels, i.e., network-level or host-level [ 20 ]. In the current Internet era, the host-level security approach is considered more secure than the network level security approach [ 16 ]. As IoT devices are low-powered and resource-constrained, host-level security mechanisms cannot be deployed.…”

Section: Introductionmentioning

confidence: 99%

“…As IoT devices are low-powered and resource-constrained, host-level security mechanisms cannot be deployed. Therefore, the network-based security mechanism is preferable for IoT devices compared to the host-based security system [ 16 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we propose the development of IoT context-aware security solutions to detect malicious traffic in IoT healthcare environments. To efficiently develop the IoT context-aware security solutions, the proposed framework consists of a newly created, open-source IoT data generator tool, named IoT-Flock [ 16 , 24 ], for generating IoT use case-specific traffic.…”

Section: Introductionmentioning

confidence: 99%

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A Framework for Malicious Traffic Detection in IoT Healthcare Environment

Hussain

Abbas

Shah

et al. 2021

Sensors

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122

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The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Framework for Malicious Traffic Detection in IoT Healthcare Environment

Hussain

Abbas

Shah

et al. 2021

Sensors

Self Cite

122

View full text Add to dashboard Cite

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“…IoT has revolutionized many innovative applications of smart technology like smart home, smart office, smart grid, smart healthcare, smart agriculture, smart transportation, smart city, etc. These applications would have a great influence in our lives thus human life will become dependent on smart devices [2].…”

Section: Introductionmentioning

confidence: 99%

Towards a Universal Features Set for IoT Botnet Attacks Detection

Hussain¹,

Fayyaz

Shah³

et al. 2020

Preprint

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The security pitfalls of IoT devices make it easy for the attackers to exploit the IoT devices and make them a part of a botnet. Once hundreds of thousands of IoT devices are compromised and become the part of a botnet, the attackers use this botnet to launch the large and complex distributed denial of service (DDoS) attacks which take down the target websites or services and make them unable to respond the legitimate users. So far, many botnet detection techniques have been proposed but their performance is limited to a specific dataset on which they are trained. This is because the features used to train a machine learning model on one botnet dataset, do not perform well on other datasets due to the diversity of attack patterns. Therefore, in this paper, we propose a universal features set to better detect the botnet attacks regardless of the underlying dataset. The proposed features set manifest preeminent results for detecting the botnet attacks when tested the trained machine learning models over three different botnet attack datasets.

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