Ensemble-based Intrusion Detection for Internet of Things Devices

Danso, Priscilla Kyei; Neto, Euclides Carlos Pinto; Dadkhah, Sajjad; Zohourian, Alireza; Molyneaux, Heather; Ghorbani, Ali A.

doi:10.1109/honet56683.2022.10019140

Cited by 10 publications

(17 citation statements)

References 20 publications

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“…Accuracy is an evaluation metric utilized to assess classification models; it represents the ratio of correctly predicted samples to the total number of samples [ 42 ]. The calculation of the mathematical equation is represented as follows:

…”

Section: Resultsmentioning

confidence: 99%

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Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments

Al Hanif,

Ilyas

2024

Sensors

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The explosive growth of the domain of the Internet of things (IoT) network devices has resulted in unparalleled ease of productivity, convenience, and automation, with Message Queuing Telemetry Transport (MQTT) protocol being widely recognized as an essential communication standard in IoT environments. MQTT enables fast and lightweight communication between IoT devices to facilitate data exchange, but this flexibility also exposes MQTT to significant security vulnerabilities and challenges that demand highly robust security. This paper aims to enhance the detection efficiency of an MQTT traffic intrusion detection system (IDS). Our proposed approach includes the development of a binary balanced MQTT dataset with an effective feature engineering and machine learning framework to enhance the security of MQTT traffic. Our feature selection analysis and comparison demonstrates that selecting a 10-feature model provides the highest effectiveness, as it shows significant advantages in terms of constant accuracy and superior training and testing times across all models. The results of this study show that the framework has the capability to enhance the efficiency of an IDS for MQTT traffic, with more than 96% accuracy, precision, recall, F1-score, and ROC, and it outperformed the most recent study that used the same dataset.

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…”

Section: Resultsmentioning

confidence: 99%

“…Recall defines the percentage of true positive predictions among the total actual number of positive instances. The calculation of the mathematical equation is represented as follows [ 42 ]:

…”

Section: Resultsmentioning

confidence: 99%

Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments

Al Hanif,

Ilyas

2024

Sensors

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“…Is every lower-level classifier for its prediction casts a vote, so the winner is the prediction with the most votes [ 58 ].…”

Section: Background Studiesmentioning

confidence: 99%

“…A learning method is employed to combine the predictions in the voting process. The resulting meta-level classifier is then utilized to construct the final prediction from the predictions of the basis classifiers [ 58 ].…”

Section: Background Studiesmentioning

confidence: 99%

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Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things’ Devices Security

Alotaibi

Ilyas

2023

Sensors

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The Internet of Things (IoT) comprises a network of interconnected nodes constantly communicating, exchanging, and transferring data over various network protocols. Studies have shown that these protocols pose a severe threat (Cyber-attacks) to the security of data transmitted due to their ease of exploitation. In this research, we aim to contribute to the literature by improving the Intrusion Detection System (IDS) detection efficiency. In order to improve the efficiency of the IDS, a binary classification of normal and abnormal IoT traffic is constructed to enhance the IDS performance. Our method employs various supervised ML algorithms and ensemble classifiers. The proposed model was trained on TON-IoT network traffic datasets. Four of the trained ML-supervised models have achieved the highest accurate outcomes; Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor. These four classifiers are fed to two ensemble approaches: voting and stacking. The ensemble approaches were evaluated using the evaluation metrics and compared for their efficacy on this classification problem. The accuracy of the ensemble classifiers was higher than that of the individual models. This improvement can be attributed to ensemble learning strategies that leverage diverse learning mechanisms with varying capabilities. By combining these strategies, we were able to enhance the reliability of our predictions while reducing the occurrence of classification errors. The experimental results show that the framework can improve the efficiency of the Intrusion Detection System, achieving an accuracy rate of 0.9863.

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A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

Lucas,

de Figueiredo,

Tojeiro

et al. 2023

IEEE Access

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Machine learning algorithms present a robust alternative for building Intrusion Detection Systems due to their ability to recognize attacks in computer network traffic by recognizing patterns in large amounts of data. Typically, classifiers are trained for this task. Together, ensemble learning algorithms have increased the performance of these detectors, reducing classification errors and allowing computer networks to be more protected. This research presents a comprehensive Systematic Review of the Literature where works related to intrusion detection with ensemble learning were obtained from the most relevant scientific bases. We offer 188 works, several compilations of datasets, classifiers, and ensemble algorithms, and document the experiments that stood out in their performance. A characteristic of this research is its originality. We found two surveys in the literature specifically focusing on the relationship between ensemble techniques and intrusion detection. We present for the last eight years covered by this survey a timeline-based view of the works studied to highlight evolutions and trends. The results obtained by our survey show a growing area, with excellent results in detecting attacks but with needs for improvement in pruning for choosing classifiers, which makes this work unprecedented for this context.

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Ensemble-based Intrusion Detection for Internet of Things Devices

Cited by 10 publications

References 20 publications

Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments

Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments

Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things’ Devices Security

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

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