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:10.21203/rs.3.rs-20786/v1

Cited by 15 publications

(46 citation statements)

References 10 publications

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“…This work aims to create MQTTset, an IoT dataset focused on MQTT communications. MQTTset was built by using IoT-Flock [ 42 ], a network traffic generator tool able to emulate IoT devices and networks based on MQTT and CoAP protocols. IoT-Flock provides the ability to configure the network scenario, in terms of nodes (e.g., sensor type, IP addresses, listening ports, etc.)…”

Section: Mqttset Datasetmentioning

confidence: 99%

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MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Vaccari

Chiola

Aiello

et al. 2020

Sensors

145

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IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts.

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Section: Mqttset Datasetmentioning

confidence: 99%

“…Differently on the previous attack, this attack tries to saturate the resources by using a single connection instead of instantiate multiple connections. This attack was generated in this case by using a module inside the IoT-Flock tool [ 42 ].…”

Section: Mqttset Datasetmentioning

confidence: 99%

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Vaccari

Chiola

Aiello

et al. 2020

Sensors

145

View full text Add to dashboard Cite

show abstract

“…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%

A Framework for Malicious Traffic Detection in IoT Healthcare Environment

Hussain

Abbas

Shah

et al. 2021

Sensors

Self Cite

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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“…Internet of Things (IoT) has inaugurated the concept of enabling our daily life objects to communicate with one another with minimal human intervention to lavish human life [1]. The burgeoning applications of IoT have initiated many innovative concepts like smart home, smart city, smart parking, smart industry, smart agriculture, etc., to make the existing systems smart, intelligent, and automated.…”

Section: Introductionmentioning

confidence: 99%

A Threat Modelling Approach to Analyze and Mitigate Botnet Attacks in Smart Home Use Case

Zahid

Hussain

Shah

et al. 2021

Preprint

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Despite the surging development and utilization of IoT devices, the security of IoT devices is still in infancy. The security pitfalls of IoT devices have made it easy for hackers to take over IoT devices and use them for malicious activities like botnet attacks. With the rampant emergence of IoT devices, botnet attacks are surging. The botnet attacks are not only catastrophic for IoT device users but also for the rest of the world. Therefore, there is a crucial need to identify and mitigate the possible threats in IoT devices during the design phase. Threat modelling is a technique that is used to identify the threats in the earlier stages of the system design activity. In this paper, we propose a threat modelling approach to analyze and mitigate the botnet attacks in an IoT smart home use case. The proposed methodology identifies the development-level and application-level threats in smart home use case using STRIDE and VAST threat modelling methods. Moreover, we reticulate the identified threats with botnet attacks. Finally, we propose the mitigation techniques for all identified threats including the botnet threats.

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

Cited by 15 publications

References 10 publications

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

A Framework for Malicious Traffic Detection in IoT Healthcare Environment

A Threat Modelling Approach to Analyze and Mitigate Botnet Attacks in Smart Home Use Case

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