Machine Learning Based IoT Intrusion Detection System: An MQTT Case Study (MQTT-IoT-IDS2020 Dataset)

Hindy, Hanan; Bayne, Ethan; Bureš, Miroslav; Atkinson, Robert; Tachtatzis, Christos; Bellekens, Xavier

doi:10.1007/978-3-030-64758-2_6

Cited by 107 publications

(97 citation statements)

References 16 publications

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“…Fortunately, such threats can be diminished using detection measures, which are known as intrusion detection. One of the well-known intrusion detection methods that has gained attention recently in the literature is using machine learning techniques [ 46 , 47 , 48 , 49 ]. As a result, the standard intrusion detection techniques show improved quality by incorporating the machine learning techniques with them [ 50 , 51 , 52 , 53 ].…”

Section: Related Workmentioning

confidence: 99%

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Qaddoura

Al-Zoubi

Faris

et al. 2021

Sensors

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The security of IoT networks is an important concern to researchers and business owners, which is taken into careful consideration due to its direct impact on the availability of the services offered by IoT devices and the privacy of the users connected with the network. An intrusion detection system ensures the security of the network and detects malicious activities attacking the network. In this study, a deep multi-layer classification approach for intrusion detection is proposed combining two stages of detection of the existence of an intrusion and the type of intrusion, along with an oversampling technique to ensure better quality of the classification results. Extensive experiments are made for different settings of the first stage and the second stage in addition to two different strategies for the oversampling technique. The experiments show that the best settings of the proposed approach include oversampling by the intrusion type identification label (ITI), 150 neurons for the Single-hidden Layer Feed-forward Neural Network (SLFN), and 2 layers and 150 neurons for LSTM. The results are compared to well-known classification techniques, which shows that the proposed technique outperforms the others in terms of the G-mean having the value of 78% compared to 75% for KNN and less than 50% for the other techniques.

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Section: Related Workmentioning

confidence: 99%

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Qaddoura

Al-Zoubi

Faris

et al. 2021

Sensors

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“…Hindy et al [67] develop the MQTT-IoT-IDS2020 dataset. This dataset comprises both common attacks and brute force attacks from the MQTT networking framework.…”

Section: ) Mqtt-iot-ids2020 Datasetmentioning

confidence: 99%

Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks

2021

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The growing development of IoT (Internet of Things) devices creates a large attack surface for cybercriminals to conduct potentially more destructive cyberattacks; as a result, the security industry has seen an exponential increase in cyber-attacks. Many of these attacks have effectively accomplished their malicious goals because intruders conduct cyber-attacks using novel and innovative techniques. An anomalybased IDS (Intrusion Detection System) uses machine learning techniques to detect and classify attacks in IoT networks. In the presence of unpredictable network technologies and various intrusion methods, traditional machine learning techniques appear inefficient. In many research areas, deep learning methods have shown their ability to identify anomalies accurately. Convolutional neural networks are an excellent alternative for anomaly detection and classification due to their ability to automatically categorize main characteristics in input data and their effectiveness in performing faster computations. In this paper, we design and develop a novel anomaly-based intrusion detection model for IoT networks. First, a convolutional neural network model is used to create a multiclass classification model. The proposed model is then implemented using convolutional neural networks in 1D, 2D, and 3D. The proposed convolutional neural network model is validated using the BoT-IoT, IoT Network Intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion detection datasets. Transfer learning is used to implement binary and multiclass classification using a convolutional neural network multiclass pre-trained model. Our proposed binary and multiclass classification models have achieved high accuracy, precision, recall, and F1 score compared to existing deep learning implementations.

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“…A very comprehensive evaluation of the effectiveness of different ML algorithms including logistic regression (LR),NB, kNN, SVM, DT, and RF to detect MQTT (Message Queuing Telemetry Transport) protocol-based attacks on IoT has been conducted in [35]. An MQTT-based novel dataset was generated and then released for research community.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Ensemble-Based Intrusion Detection System for Internet of Things

et al. 2021

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The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model’s performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios.

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Machine Learning Based IoT Intrusion Detection System: An MQTT Case Study (MQTT-IoT-IDS2020 Dataset)

Cited by 107 publications

References 16 publications

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks

A New Ensemble-Based Intrusion Detection System for Internet of Things

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