Análise de Tráfego de Rede com Machine Learning para Identificação de Ameaças a Dispositivos IoT

Aragão, Marcelo V C; Mafra, Samuel; Figueiredo, Felipe Augusto Pereira de

doi:10.14209/sbrt.2022.1570824939

Cited by 4 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In contrast, LightGBM is hyperparameter-sensitive and less precise, while XGBoost is slower but more robust. Interestingly, the obtained classification results align with Aragão, Mafra, and Figueiredo [1], where a DT proved to be the fastest and most accurate model for threat identification, despite using a different dataset.…”

Section: Experiments and Discussionsupporting

confidence: 72%

See 1 more Smart Citation

ML-based Novelty Detection and Classification of IoT Threats using Network Traffic Analysis

Aragão,

Ambrósio,

Figueiredo

2023

Anais Do XLI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

View full text Add to dashboard Cite

This article presents a practical evaluation of machine-learning models to detect novelties and classify threats in IoT networks using an ML-based approach. Given the escalating significance of analyzing network traffic amidst the proliferation of devices and sensitive data exchange, this research holds significant relevance. The IoT Network Intrusion dataset was chosen for experimentation, followed by data processing and imbalance handling techniques. Four distinct models encompassing novelty detection and classification were trained, allowing for an in-depth comparison of their performance in terms of accuracy and time. Notably, after attaining the results, it was evident that these models achieved remarkably high accuracy in novelty detection and classification tasks, emphasizing that techniques based on machine learning can be successfully applied to this context.

show abstract

Section: Experiments and Discussionsupporting

confidence: 72%

“…Detecting network traffic novelties has become crucial with the rapid increase in connected devices and the exchange of sensitive data over networks [1]. This article introduces a novel approach that combines supervised and unsupervised machine learning (ML) techniques to address this challenge.…”

Section: Introductionmentioning

confidence: 99%

ML-based Novelty Detection and Classification of IoT Threats using Network Traffic Analysis

Aragão,

Ambrósio,

Figueiredo

2023

Anais Do XLI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

View full text Add to dashboard Cite

show abstract

“…This model was chosen based on results obtained and published in [ 49 ]. DT models are one of the simplest yet most successful and powerful forms of ML models [ 50 ].…”

Section: Proposed Methodsmentioning

confidence: 99%

Simultaneous Estimation of Azimuth and Elevation Angles Using a Decision Tree-Based Method

Carballeira

Figueiredo

Brito

2023

Sensors

View full text Add to dashboard Cite

This study addresses the problem of accurately predicting azimuth and elevation angles of signals impinging on an antenna array employing Machine Learning (ML). Using the information obtained at a receiving system when a transmitter’s signal hits it, a Decision Tree (DT) model is trained to estimate azimuth and elevation angles simultaneously. Simulation results demonstrate the robustness of the proposed DT-based method, showcasing its ability to predict the Direction of Arrival (DOA) in diverse conditions beyond the ones present in the training dataset, i.e., the results display the model’s generalization capability. Additionally, the comparative analysis reveals that DT-based DOA estimation outperforms the state-of-the-art MUltiple SIgnal Classification (MUSIC) algorithm. Our results demonstrate an average reduction of over 90% in the prediction error and 50% in the prediction time achieved by our proposal when compared to the MUSIC algorithm. These results establish DTs as competitive alternatives for DOA estimation in signal reception systems.

show abstract

“…"Time" here can have two meanings: "training time", the time taken for the model(s) to fit with the data, and "testing/prediction time", which is the time taken to classify all samples in the test set. This study will not focus on the latter metric since the fast prediction is only crucial in real-time scenarios, such as in Network Traffic Analysis (NTA)-based systems (Aragão et al, 2022(Aragão et al, , 2023. However, the training time is an important analytical aspect since impractical training times can lead to improper (or even unavailable) models.…”

Section: Recall(classmentioning

confidence: 99%

A practical evaluation of AutoML tools for binary, multiclass, and multilabel classification

Aragão,

Afonso,

Ferraz

et al. 2024

Preprint

View full text Add to dashboard Cite

Choosing the right Automated Machine Learning (AutoML) tool is crucial for researchers of varying expertise to achieve optimal performance in diverse classification tasks. However, the abundance of AutoML frameworks with varying features makes selection challenging. This study addresses this gap by conducting a practical evaluation informed by a theoretical and bibliographical review and a feature-based comparison of twelve AutoML frameworks. The evaluation, conducted under time constraints, assessed accuracy and training efficiency across binary, multiclass, and multilabel (considering both native and label powerset representations) classification tasks on fifteen datasets. We acknowledge limitations, including dataset scope and default parameter usage, which may not capture the full potential of some frameworks. Our findings reveal no single ``perfect'' tool, as frameworks prioritize accuracy or speed. For time-sensitive binary/multiclass tasks, \claas, \autogluon, and \autokeras showed promise. In multilabel scenarios, \autosklearn offered higher accuracy, while \autokeras excelled in training speed. These results highlight the crucial trade-off between accuracy and speed, emphasizing the importance of considering both factors during tool selection for binary, multiclass, and multilabel classification problems. We made the code, experiment reproduction instructions, and outcomes publicly available on GitHub.

show abstract

Análise de Tráfego de Rede com Machine Learning para Identificação de Ameaças a Dispositivos IoT

Cited by 4 publications

References 16 publications

ML-based Novelty Detection and Classification of IoT Threats using Network Traffic Analysis

ML-based Novelty Detection and Classification of IoT Threats using Network Traffic Analysis

Simultaneous Estimation of Azimuth and Elevation Angles Using a Decision Tree-Based Method

A practical evaluation of AutoML tools for binary, multiclass, and multilabel classification

Contact Info

Product

Resources

About