XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection

Faysal, Jabed Al; Mostafa, Sk Tahmid; Tamanna, Jannatul Sultana; Mumenin, Khondoker Mirazul; Arifin, Md. Mashrur; Awal, Md. Abdul; Shome, Atanu; Mostafa, Sheikh Shanawaz

doi:10.3390/telecom3010003

Cited by 46 publications

(13 citation statements)

References 34 publications

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“…Parra et al has created an LSTM model with the help of correlation-based feature selection to classify the attacks and confirmed their model with 75 features achieved 97.84% accuracy, 97.81% precision, 95% Recall, 96.25% F1-score [56]. Faysal et al proposed an XGBoost model that utilized 40 related features and stated that the model classified attacks with 99.96% accuracy and 99.94% F1-score [55].…”

Section: Discussionmentioning

confidence: 99%

In-Depth Feature Selection for the Statistical Machine Learning-Based Botnet Detection in IoT Networks

2022

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The attackers compromise insecure IoT devices to enlarge their botnets for the purpose of launching more influential attacks against their victims. In various studies, it is demonstrated that machine learning can be utilized for the detection of IoT botnet attacks. In this paper, we focus on the minimization of feature sets for machine learning tasks that are formulated as six different binary and multi-class classification problems based on the stages of the botnet life-cycle. More specifically, we apply filter and wrapper methods with some machine learning methods and derived the optimal feature sets for each classification problem. The experimental results show that it is possible to achieve very high detection rates with very limited number of features. Some wrapper methods guarantee an optimal feature set regardless of the problem formulation but filter methods do not achieve that in all cases. The feature selection methods more prefer channel-based features for the detection at post-attack and communication & control stages whereas host-based features are more influential for identifying the attacks originating from the bots. INDEX TERMSFeature selection, machine learning, Internet of Things, botnet, intrusion detection I. INTRODUCTION

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Section: Discussionmentioning

confidence: 99%

In-Depth Feature Selection for the Statistical Machine Learning-Based Botnet Detection in IoT Networks

2022

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“…In [11], a hybrid ML technique named extreme gradient boosting with random forest (XGB-RF) was presented to detect intrusion attacks. The presented hybrid system can be executed to the N-BaIoT database comprising hazardous botnet attacks.…”

Section: Related Workmentioning

confidence: 99%

“…In Eq. (11), h(j) refers to the outcomes of j th hidden states, a j indicates the scaling feature of the wavelet basis function (WBF), b j denotes the translation factor of WBF h j , and h j has the wavelet basis function. In such cases, the Morlet WBF has applied as a function of hidden state node:…”

Section: Intrusion Detection Using Optimal Wnnmentioning

confidence: 99%

Optimal Wavelet Neural Network-Based Intrusion Detection in Internet of Things Environment

Mohamed¹,

Alrowais²,

Al-Hagery³

et al. 2023

Computers, Materials &Amp; Continua

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As the Internet of Things (IoT) endures to develop, a huge count of data has been created. An IoT platform is rather sensitive to security challenges as individual data can be leaked, or sensor data could be used to cause accidents. As typical intrusion detection system (IDS) studies can be frequently designed for working well on databases, it can be unknown if they intend to work well in altering network environments. Machine learning (ML) techniques are depicted to have a higher capacity at assisting mitigate an attack on IoT device and another edge system with reasonable accuracy. This article introduces a new Bird Swarm Algorithm with Wavelet Neural Network for Intrusion Detection (BSAWNN-ID) in the IoT platform. The main intention of the BSAWNN-ID algorithm lies in detecting and classifying intrusions in the IoT platform. The BSAWNN-ID technique primarily designs a feature subset selection using the coyote optimization algorithm (FSS-COA) to attain this. Next, to detect intrusions, the WNN model is utilized. At last, the WNN parameters are optimally modified by the use of BSA. A widespread experiment is performed to depict the better performance of the BSAWNN-ID technique. The resultant values indicated the better performance of the BSAWNN-ID technique over other models, with an accuracy of 99.64% on the UNSW-NB15 dataset.

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“…RFECV starts with all features and gradually eliminates less important features in iterative fashion based on model performance. It evaluates the impact of the features' removals using cross-validation, ensuring an unbiased assessment (Faysal, 2022).…”

Section: Machine-learning Approaches and Prediction Performance Evalu...mentioning

confidence: 99%

An Explainable Machine Learning Framework for Predicting the Risk of Buprenorphine Treatment Discontinuation for Opioid Use Disorder

Faysal,

Noor-E-Alam,

Young

et al. 2023

Preprint

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ObjectivesBuprenorphine is an effective evidence-based medication for opioid use disorder (OUD). Yet premature discontinuation undermines treatment effectiveness, increasing risk of mortality and overdose. We developed and evaluated a machine learning (ML) framework for predicting buprenorphine care discontinuity within 1-year following treatment initiation.MethodsThis retrospective study used United States 2018-2021 MarketScan commercial claims data of insured individuals aged 18-64 who initiated buprenorphine between July 2018 and December 2020 with no buprenorphine prescriptions in the previous six months. We measured buprenorphine prescription discontinuation gaps of ≥30 days within the first year of initiating treatment. We developed predictive models employing logistic regression, decision tree classifier, random forest, XGBoost, Adaboost, and random forest-XGBoost ensemble. We applied recursive feature elimination with cross-validation to reduce dimensionality and identify the most predictive features while maintaining model robustness. We focused on two distinct treatment stages: at the time of treatment initiation and one and three months after treatment initiation. We employed SHapley Additive exPlanations (SHAP) analysis that helped us explain the contributions of different features in predicting buprenorphine discontinuation. We stratified patients into risk subgroups based on their predicted likelihood of treatment discontinuation, dividing them into decile subgroups. Additionally, we used a calibration plot to analyze the reliability of the models.ResultsA total of 30,373 patients initiated buprenorphine and 14.98% (4,551) discontinued treatment. C-statistic varied between 0.56 and 0.76 for the first-stage models including patient-level demographic and clinical variables. Inclusion of proportion of days covered (PDC) measured at one-month and three-month following treatment initiation significantly increased the models’ discriminative power (C-statistics: 0.60 to 0.82). Random forest (C-statistics: 0.76, 0.79 and 0.82 with baseline predictors, one-month PDC and three-month PDC, respectively) outperformed other ML models in discriminative performance in all stages (C-statistics: 0.56 to 0.77). Most influential risk factors of discontinuation included early stage medication adherence, age, and initial days of supply.ConclusionML algorithms demonstrated a good discriminative power in identifying patients at higher risk of buprenorphine care discontinuity. The proposed framework may help healthcare providers optimize treatment strategies and deliver targeted interventions to improve buprenorphine care continuity.

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XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection

Cited by 46 publications

References 34 publications

In-Depth Feature Selection for the Statistical Machine Learning-Based Botnet Detection in IoT Networks

In-Depth Feature Selection for the Statistical Machine Learning-Based Botnet Detection in IoT Networks

Optimal Wavelet Neural Network-Based Intrusion Detection in Internet of Things Environment

An Explainable Machine Learning Framework for Predicting the Risk of Buprenorphine Treatment Discontinuation for Opioid Use Disorder

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