Expectile Regression on Distributed Large-Scale Data

Hu, Aijun; Li, Chujin; Wu, Jing

doi:10.1109/access.2020.3006526

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…One notable advantage of DRF is its ability to utilize nonlinear algorithms. Additionally, it can provide valuable feedback on the importance of each predictor in the algorithm, making it a robust choice for handling noisy data [ 26 , 27 , 28 , 29 ]. When employing DRF for MQTT attack detection, a series of five steps are typically followed.…”

Section: Implementation and Resultsmentioning

confidence: 99%

Secure Enhancement for MQTT Protocol Using Distributed Machine Learning Framework

Alotaibi,

Sayed Ahmed,

Kamel

et al. 2024

Sensors

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The Message Queuing Telemetry Transport (MQTT) protocol stands out as one of the foremost and widely recognized messaging protocols in the field. It is often used to transfer and manage data between devices and is extensively employed for applications ranging from smart homes and industrial automation to healthcare and transportation systems. However, it lacks built-in security features, thereby making it vulnerable to many types of attacks such as man-in-the-middle (MitM), buffer overflow, pre-shared key, brute force authentication, malformed data, distributed denial-of-service (DDoS) attacks, and MQTT publish flood attacks. Traditional methods for detecting MQTT attacks, such as deep neural networks (DNNs), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and fuzzy logic, may exist. The increasing prevalence of device connectivity, sensor usage, and environmental scalability become the most challenging aspects that novel detection approaches need to address. This paper presents a new solution that leverages an H2O-based distributed machine learning (ML) framework to improve the security of the MQTT protocol in networks, particularly in IoT environments. The proposed approach leverages the strengths of the H2O algorithm and architecture to enable real-time monitoring and distributed detection and classification of anomalous behavior (deviations from expected activity patterns). By harnessing H2O’s algorithms, the identification and timely mitigation of potential security threats are achieved. Various H2O algorithms, including random forests, generalized linear models (GLMs), gradient boosting machine (GBM), XGBoost, and the deep learning (DL) algorithm, have been assessed to determine the most reliable algorithm in terms of detection performance. This study encompasses the development of the proposed algorithm, including implementation details and evaluation results. To assess the proposed model, various evaluation metrics such as mean squared error (MSE), root-mean-square error (RMSE), mean per class error (MCE), and log loss are employed. The results obtained indicate that the H2OXGBoost algorithm outperforms other H2O models in terms of accuracy. This research contributes to the advancement of secure IoT networks and offers a practical approach to enhancing the security of MQTT communication channels through distributed detection and classification techniques.

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Section: Implementation and Resultsmentioning

confidence: 99%

Secure Enhancement for MQTT Protocol Using Distributed Machine Learning Framework

Alotaibi,

Sayed Ahmed,

Kamel

et al. 2024

Sensors

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“…Tan et al (2022) also proposed the communication‐constrained distributed QR estimator using a double‐smoothing approach. Hu et al (2020) and Pan (2021) considered the CSL framework for ER model with the SCAD and adaptive Lasso penalties. However, all these distributed methods only work well when the local sample size is larger than the dimension of covariates and may lead to bias in high‐dimensional regression models.…”

Section: Introductionmentioning

confidence: 99%

Communication‐efficient low‐dimensional parameter estimation and inference for high‐dimensional Lp$$ {L}^p $$‐quantile regression

Gao,

Wang

2023

Scandinavian J Statistics

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The Lp‐quantile regression generalizes both quantile regression and expectile regression, and has become popular for its robustness and effectiveness especially when 1 < p ≤ 2. In this paper, we consider the data that are inherently distributed and propose two distributed Lp‐quantile regression estimators for a preconceived low‐dimensional parameter in the presence of high‐dimensional extraneous covariates. To handle the impact of high‐dimensional nuisance parameters, we first investigate regularized projection score for estimating low‐dimensional parameter of main interest in Lp‐quantile regression. To deal with the distributed data, we further propose two communication‐efficient surrogate projection score estimators and establish their theoretical properties. The finite‐sample performance of the proposed estimators is studied through simulations and an application to Communities and Crime data set is also presented.This article is protected by copyright. All rights reserved.

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Expectile Regression on Distributed Large-Scale Data

Cited by 2 publications

References 32 publications

Secure Enhancement for MQTT Protocol Using Distributed Machine Learning Framework

Secure Enhancement for MQTT Protocol Using Distributed Machine Learning Framework

Communication‐efficient low‐dimensional parameter estimation and inference for high‐dimensional Lp$$ {L}^p $$‐quantile regression

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