CREME: A toolchain of automatic dataset collection for machine learning in intrusion detection

Bui, Huu Khoi; Lin, Ying-Dar; Hwang, Ren-Junn; Lin, Po Ching; Nguyen, Van-Linh; Lai, Yuan Cheng

doi:10.1016/j.jnca.2021.103212

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…Additionally, the paper states that future research should focus on detecting unbalanced and new data types and finding or building good algorithms for this purpose. Bui et al [72] describe a toolchain that automates the process of feature extraction, data labeling, and assessing the quality of created datasets from various sources, including network traffic, system logs, and monitoring software reports. It also allows for the validation and customization of datasets.…”

Section: Data Lackmentioning

confidence: 99%

Overcoming the Challenges of Data Lack, Leakage, and Dimensionality in Intrusion Detection Systems: A Comprehensive Review

Bouke,

Abdullah,

Udzir

et al. 2024

JCIS

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The Internet of Things (IoT) and cloud computing are rapidly gaining momentum as decentralized internet-based technologies and have led to an increase in information in nearly every technical and commercial industry. However, ensuring the security of IoT systems is a pressing issue due to the complexities involved in connected and shared environments. Networks are guarded by Intrusion Detection Systems (IDS) against various cyber threats such as malware, viruses, and unauthorized access. IDS have recently adopted Machine Learning (ML) and Deep Learning (DL) techniques to identify and classify security risks. However, the effective utilization of these technologies depends on the availability, quality, and characteristics of the data used to train models. Moreover, data lack, data leak, and dimensionality (DLLD) are common problems in data science and ML. This paper surveys existing research and suggests solutions for overcoming DLLD-related issues to improve IDS model.

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Section: Data Lackmentioning

confidence: 99%

Overcoming the Challenges of Data Lack, Leakage, and Dimensionality in Intrusion Detection Systems: A Comprehensive Review

Bouke,

Abdullah,

Udzir

et al. 2024

JCIS

View full text Add to dashboard Cite

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“…A new automated toolchain, called CREAM, has been created by the developers of [25]. This toolchain combines various tools to automate the whole process of configuration, attack and benign behavior reproduction, data collection, feature extraction, data labeling, and evaluation.…”

Section: Dataset Collectionmentioning

confidence: 99%

IoT Traffic Analyzer Tool with Automated and Holistic Feature Extraction Capability

Subahi

Almasre

2023

Sensors

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The Internet of Things (IoT) is an emerging technology that attracted considerable attention in the last decade to become one of the most researched topics in computer science studies. This research aims to develop a benchmark framework for a public multi-task IoT traffic analyzer tool that holistically extracts network traffic features from an IoT device in a smart home environment that researchers in various IoT industries can implement to collect information about IoT network behavior. A custom testbed with four IoT devices is created to collect real-time network traffic data based on seventeen comprehensive scenarios of these devices’ possible interactions. The output data is fed into the IoT traffic analyzer tool for both flow and packet levels analysis to extract all possible features. Such features are ultimately classified into five categories: IoT device type, IoT device behavior, Human interaction type, IoT behavior within the network, and Abnormal behavior. The tool is then evaluated by 20 users considering three variables: usefulness, accuracy of information being extracted, performance and usability. Users in three groups were highly satisfied with the interface and ease of use of the tool, with scores ranging from 90.5% to 93.8% and with an average score between 4.52 and 4.69 with a low standard deviation range, indicating that most of the data revolve around the mean

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“…Weinger et al [8], applied their proposed data augmentation method to DS2OS and ToN_IoT datasets for intrusion detection on IoT networks. Bui et al [9], established a toolchain called Configuration, REproduction, Multi-dataset, and Evaluation (CREME) to increase the intrusion detection capabilities of IDS, and measured both a new dataset and the quality of the dataset they created. Haider et al [10], proposed the Fuzzy Gaussian Mixture-based Correntropy-Host Anomaly Detection Systems (FGMC-HADS) method based on the Fuzzy Rough Attribute Reduction (FRAR) method and the Gaussian Mixture Model (GMM).…”

Section: Introduction (Gi̇ri̇ş)mentioning

confidence: 99%

A new Intrusion Detection System for Secured IoT/IIoT Networks based on LGBM

Fırat

KATAR

2023

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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The Internet of Things (IoT) is one of the technologies used in many fields today. Cyber attacks against IoT/Industrial IoT (IIoT) networks, which are increasingly used thanks to the convenience it provides, are constantly increasing. Detection of attacks against IoT/IIoT networks is one of the popular topics recently. The development of a dataset for IoT applications is essential for the intrusion detection in IoT networks. In this context, the ToN_IoT dataset created in the laboratory of UNSW Canberra (Australia) is one of the most comprehensive datasets that can be used to detect cyber attacks on IoT networks. In this study, fridge, garage door, GPS tracker, modbus, motion light, weather, thermostat datasets related to IoT sensors from ToN_IoT datasets were used. The datasets used were subjected to multi-class classification with the Light Gradient Boosting Machine (LGBM) classifier proposed in the study. The obtained results were compared with the literature and it was seen that the proposed method provided the highest classification performance in the literature. It has been determined that the proposed method is effective in preventing cyber attacks on IoT/IIoT networks.

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CREME: A toolchain of automatic dataset collection for machine learning in intrusion detection

Cited by 10 publications

References 12 publications

Overcoming the Challenges of Data Lack, Leakage, and Dimensionality in Intrusion Detection Systems: A Comprehensive Review

Overcoming the Challenges of Data Lack, Leakage, and Dimensionality in Intrusion Detection Systems: A Comprehensive Review

IoT Traffic Analyzer Tool with Automated and Holistic Feature Extraction Capability

A new Intrusion Detection System for Secured IoT/IIoT Networks based on LGBM

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