Using MedBIoT Dataset to Build Effective Machine Learning-Based IoT Botnet Detection Systems

Guerra-Manzanares, Alejandro; Medina-Galindo, Jorge; Bahşi, Hayretdin; Nõmm, Sven

doi:10.1007/978-3-030-94900-6_11

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…We also tested on datasets from different sources and found that our image-based deep learning approach consistently outperformed traditional methods by a wide margin. We tested both of our trained models on, USTC-TFC2016 [3], and Taltech.ee MedBIoT [34], and Mirai-based Multi-Class [29] datasets. They are much smaller datasets than the MCFP dataset containing only about 11 malware classes which are a subset of MCFP's malware classes.…”

Section: Resultsmentioning

confidence: 99%

Classifying Malware Traffic Using Images and Deep Convolutional Neural Network

Davis,

Xu,

Roy

2024

IEEE Access

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Network traffic classification plays a crucial role in detecting malware threats. However, most existing research focuses on extracting statistical features from the network traffic, ignoring the rich information contained within raw packet capture (pcap) files. To achieve higher accuracy in malware traffic classification, we propose a novel approach that fully utilizes the information contained in the pcap files by representing them with images and then train deep Convolutional Neural Networks (CNN) to learn the features automatically and classify them with higher accuracy. We transform selected fields of the IP headers in network sessions into 50x50 RGB images. These images serve as input to CNN, and malware samples are grouped by class or malware name. The model is initially trained and validated on the MCFP dataset with more than 140 malware classes and subsequently tested on separate datasets, namely USTC-TFC2016, Taltech.ee MedBIoT, and IEEE-Mirai. The macro F1 scores and accuracy of our method are significantly higher than the baseline statistical-feature based approach both in the validation dataset and in the test datasets from different sources. The result of this research has potential to be extended beyond malware classification to enable the classification of various types network traffic data.

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Section: Resultsmentioning

confidence: 99%

Classifying Malware Traffic Using Images and Deep Convolutional Neural Network

Davis,

Xu,

Roy

2024

IEEE Access

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“…A research study [26] produced a MedBIoT dataset containing both typical and botnet traffic in the IoT network. The dataset includes data from the primal phase of botnet preparation and features real botnet malware such as Mirai, BASHLITE, and Torii.…”

Section: Related Workmentioning

confidence: 99%

Efficient IoT Security: Weighted Voting for BASHLITE and Mirai Attack Detection

Abu-Zanona

2023

IJACSA

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The increasing number of devices in the Internet of Things (IoT) has exposed various vulnerabilities, such as BASHLITE and Mirai attacks, making it easier for cyber threats to emerge. Due to these vulnerabilities, developing innovative detection and mitigation strategies is essential. Our proposed solution is an ensemble-based weighted voting model that combines different classifiers, including Random Forest, eXtreme Gradient Boosting (XGBoost), Gradient Boosting, K-nearest neighbor (KNN), Multilayer Perceptron (MLP), and Adaptive Boosting (AdaBoost), using artificial intelligence and machine learning. We evaluated our model on the N-BaIoT dataset, a benchmark in this domain. Our results show that the weighted voting approach has exceptional accuracy, precision, recall, and F1-Score. This highlights the effectiveness of our model in classifying various attack instances within the IoT security context. Our approach performs better than other state-of-the-art methods, achieving a remarkable accuracy of 99.9955% in detecting and preventing BASHLITE and Mirai cyber-attacks on IoT devices.

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Using MedBIoT Dataset to Build Effective Machine Learning-Based IoT Botnet Detection Systems

Cited by 2 publications

References 30 publications

Classifying Malware Traffic Using Images and Deep Convolutional Neural Network

Classifying Malware Traffic Using Images and Deep Convolutional Neural Network

Efficient IoT Security: Weighted Voting for BASHLITE and Mirai Attack Detection

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