MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Vaccari, Ivan; Chiola, Giovanni; Aiello, Maurizio; Mongelli, Maurizio; Cambiaso, Enrico

doi:10.3390/s20226578

Cited by 170 publications

(119 citation statements)

References 55 publications

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“…After the normal network started generating the IoT traffic, we created an invader network. The invader network includes ten attacking devices that are generating four types of attacks, including an MQTT distributed denial-of-service, MQTT publish flood, brute force, and SlowITE [ 37 ] attack. The following sections describe the types of attack that IoT-Flock [ 16 , 24 ] supports.…”

Section: Proposed Frameworkmentioning

confidence: 99%

“…Using the LR algorithm, we selected the ten most significant features to train and test the machine learning models: [’frame.time_delta’, ’tcp.time_delta’, ’tcp.flags.ack’, ’tcp.flags.push’, ’tcp.flags.reset’, ’mqtt.hdrflags’, ’mqtt.msgtype’, ’mqtt.qos’, ’mqtt.retain’, ’mqtt.ver’]. The details of these features are given in [ 37 ].…”

Section: Proposed Frameworkmentioning

confidence: 99%

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A Framework for Malicious Traffic Detection in IoT Healthcare Environment

Hussain

Abbas

Shah

et al. 2021

Sensors

136

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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: Proposed Frameworkmentioning

confidence: 99%

Section: Proposed Frameworkmentioning

confidence: 99%

A Framework for Malicious Traffic Detection in IoT Healthcare Environment

Hussain

Abbas

Shah

et al. 2021

Sensors

136

View full text Add to dashboard Cite

show abstract

“…It has both common network scanning attacks and brute-force attacks. MQTT protocol communication datasets are widely adopted [33], [34] for building an effective Intrusion Detection model for IoT devices.…”

Section: B Datasetmentioning

confidence: 99%

An Ensemble Multi-View Federated Learning Intrusion Detection for IoT

et al. 2021

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The rise in popularity of Internet of Things (IoT) devices has attracted hackers to develop IoT-specific attacks. The microservice architecture of IoT devices relies on the Internet to provide their intended services. An unguarded IoT network makes inter-connected devices vulnerable to attacks. It will be a tedious and ineffective process to manually detect the attacks in the network, as the attackers frequently upgrade their attack strategies. Machine learning (ML)-assisted approaches have been proposed to build intrusion detection for cybersecurity automation in IoT networks. However, most such approaches focus on training an ML model using a single view of the dataset, which often fails to build insightful knowledge and understand each feature's impact on the ML model's decision-making ability. As such, the model training with a single view may result in an incomplete understanding of patterns in large feature-set datasets. Moreover, the current approaches are mainly designed in a centralized manner in which the raw data is transferred from the edge devices to the central server for training. This, in turn, may expose the data to all kinds of attacks without adhering to the privacy-preserving of data security. Multi-view learning has gained popularity for its ability to learn from different data views and deliver efficient performance with more distinguished predictions. This paper proposes a federated learning-based intrusion detection approach, called MV-FLID, that trains on multiple views of IoT network data in a decentralized format to detect, classify, and defend against attacks. The multi-view ensemble learning aspect helps in maximizing the learning efficiency of different classes of attacks. The Federated Learning (FL) aspect, wherein the device's data is not shared to the server, performs profile aggregation efficiently with the benefit of peer learning. Our evaluation results show that our proposed approach has higher accuracy compared to the traditional non-FL centralized approach.INDEX TERMS Internet of Things, IoT security, federated learning, neural networks, multi-view classification, intrusion detection system.

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“…The data set was developed on a realistic testbed, and it contains simulated and legitimate IoT network traffic with different types of attacks. The authors in [ 44 ] presented a data set named as MQTTset, which is related to the MQTT protocol. The authors implemented different machine learning algorithms to validate the data set.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Deep Learning-Driven SDN Enabled Mechanism for Secure Communication in Internet of Things (IoT)

Javeed

Khan

Ahmad

2021

Sensors

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The Internet of Things (IoT) has emerged as a new technological world connecting billions of devices. Despite providing several benefits, the heterogeneous nature and the extensive connectivity of the devices make it a target of different cyberattacks that result in data breach and financial loss. There is a severe need to secure the IoT environment from such attacks. In this paper, an SDN-enabled deep-learning-driven framework is proposed for threats detection in an IoT environment. The state-of-the-art Cuda-deep neural network, gated recurrent unit (Cu- DNNGRU), and Cuda-bidirectional long short-term memory (Cu-BLSTM) classifiers are adopted for effective threat detection. We have performed 10 folds cross-validation to show the unbiasedness of results. The up-to-date publicly available CICIDS2018 data set is introduced to train our hybrid model. The achieved accuracy of the proposed scheme is 99.87%, with a recall of 99.96%. Furthermore, we compare the proposed hybrid model with Cuda-Gated Recurrent Unit, Long short term memory (Cu-GRULSTM) and Cuda-Deep Neural Network, Long short term memory (Cu- DNNLSTM), as well as with existing benchmark classifiers. Our proposed mechanism achieves impressive results in terms of accuracy, F1-score, precision, speed efficiency, and other evaluation metrics.

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MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Cited by 170 publications

References 55 publications

A Framework for Malicious Traffic Detection in IoT Healthcare Environment

A Framework for Malicious Traffic Detection in IoT Healthcare Environment

An Ensemble Multi-View Federated Learning Intrusion Detection for IoT

A Hybrid Deep Learning-Driven SDN Enabled Mechanism for Secure Communication in Internet of Things (IoT)

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