An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things

Rani, Deepti; Gill, Nasib Singh; Gulia, Preeti; Chatterjee, Jyotir Moy

doi:10.1155/2022/1668676

Cited by 18 publications

(17 citation statements)

References 67 publications

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“…Stacking extends the bagging workflow by building a meta-model that combines, in an optimal manner, the final predictions of base models. Voting trains multiple base models in which initial predictions are determined independently by each model, and the final prediction is selected through averaging or majority voting [ 39 ]. In this study, stacking and voting are employed using MLP , SVM , and LR models that prove their efficiency individually in an IoT-based IDS context.…”

Section: A Proposed Robust Ensemble Adversarial Machine Learning Fram...mentioning

confidence: 99%

RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic

Alkadi,

Al-Ahmadi,

Ben Ismail

2024

Sensors

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Recently, Machine Learning (ML)-based solutions have been widely adopted to tackle the wide range of security challenges that have affected the progress of the Internet of Things (IoT) in various domains. Despite the reported promising results, the ML-based Intrusion Detection System (IDS) proved to be vulnerable to adversarial examples, which pose an increasing threat. In fact, attackers employ Adversarial Machine Learning (AML) to cause severe performance degradation and thereby evade detection systems. This promoted the need for reliable defense strategies to handle performance and ensure secure networks. This work introduces RobEns, a robust ensemble framework that aims at: (i) exploiting state-of-the-art ML-based models alongside ensemble models for IDSs in the IoT network; (ii) investigating the impact of evasion AML attacks against the provided models within a black-box scenario; and (iii) evaluating the robustness of the considered models after deploying relevant defense methods. In particular, four typical AML attacks are considered to investigate six ML-based IDSs using three benchmarking datasets. Moreover, multi-class classification scenarios are designed to assess the performance of each attack type. The experiments indicated a drastic drop in detection accuracy for some attempts. To harden the IDS even further, two defense mechanisms were derived from both data-based and model-based methods. Specifically, these methods relied on feature squeezing as well as adversarial training defense strategies. They yielded promising results, enhanced robustness, and maintained standard accuracy in the presence or absence of adversaries. The obtained results proved the efficiency of the proposed framework in robustifying IDS performance within the IoT context. In particular, the accuracy reached 100% for black-box attack scenarios while preserving the accuracy in the absence of attacks as well.

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Section: A Proposed Robust Ensemble Adversarial Machine Learning Fram...mentioning

confidence: 99%

RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic

Alkadi,

Al-Ahmadi,

Ben Ismail

2024

Sensors

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“…40 Bagging is the term for bootstrap aggregation, an ensemble learning that generates data using sequential and parallel approaches. 41 In most cases, bagging uses a similar weak classifier and trains them simultaneously. 41 Several decision tree learners are used in this ensemble; each is trained on a different portion of the training set.…”

Section: Quadratic Support Vector Machine (Qsvm)mentioning

confidence: 99%

“…41 In most cases, bagging uses a similar weak classifier and trains them simultaneously. 41 Several decision tree learners are used in this ensemble; each is trained on a different portion of the training set. Furthermore, the majority vote of the estimations is used to make the final prediction of an EBT, for instance, through the individual decision trees.…”

Section: Quadratic Support Vector Machine (Qsvm)mentioning

confidence: 99%

The implication of oversampling on the effectiveness of force signals in the fault detection of endodontic instruments during RCT

Thakur

Kankar

Parey

et al. 2023

Proc Inst Mech Eng H

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This work provides an innovative endodontic instrument fault detection methodology during root canal treatment (RCT). Sometimes, an endodontic instrument is prone to fracture from the tip, for causes uncertain the dentist’s control. A comprehensive assessment and decision support system for an endodontist may avoid several breakages. This research proposes a machine learning and artificial intelligence-based approach that can help to diagnose instrument health. During the RCT, force signals are recorded using a dynamometer. From the acquired signals, statistical features are extracted. Because there are fewer instances of the minority class (i.e. faulty/moderate class), oversampling of datasets is required to avoid bias and overfitting. Therefore, the synthetic minority oversampling technique (SMOTE) is employed to increase the minority class. Further, evaluating the performance using the machine learning techniques, namely Gaussian Naïve Bayes (GNB), quadratic support vector machine (QSVM), fine k-nearest neighbor (FKNN), and ensemble bagged tree (EBT). The EBT model provides excellent performance relative to the GNB, QSVM, and FKNN. Machine learning (ML) algorithms can accurately detect endodontic instruments’ faults by monitoring the force signals. The EBT and FKNN classifier is trained exceptionally well with an area under curve values of 1.0 and 0.99 and prediction accuracy of 98.95 and 97.56%, respectively. ML can potentially enhance clinical outcomes, boost learning, decrease process malfunctions, increase treatment efficacy, and enhance instrument performance, contributing to superior RCT processes. This work uses ML methodologies for fault detection of endodontic instruments, providing practitioners with an adequate decision support system.

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“…RF is one of ensemble learning, which is rapidly used for land-cover categorization from sensed data, as well as other domains connected to the environment and water resources [38]. An ensemble of regression (or classification) tree models used in the RF algorithm technique and a succession of separate trees is constructed based on random sub samples from the original data [39]. Each subsample has a decision tree, which is used to forecast the response variable (or a class).…”

Section: Assimilation Of Iot and ML (References)mentioning

confidence: 99%

Big Data Analysis Framework for Water Quality Indicators with Assimilation of IoT and ML

et al. 2022

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According to the United Nations, the Sustainable Development Goal ‘6’ seeks to ensure the availability and sustainable management of water for all. Digital technologies, such as big data, Internet of Things (IoT), and machine learning (ML) have a significant role and capability to meet the goal. Water quality analysis in any region is critical to identify and understand the standard of water quality and the quality of water is analyzed based on water quality parameters (WQP). Currently, water pollution and the scarcity of water are two major concerns in the region of Uttarakhand, and the analysis of water before it is supplied for human consumption has gained attention. In this study, a big data analytics framework is proposed to analyze the water quality parameters of 13 districts of Uttarakhand and find the correlation among the parameters with the assimilation of IoT and ML. During the analysis, statistical and fractal methods are implemented to understand the anomalies between the water quality parameters in 13 districts of Uttarakhand. The variation in WQP is analyzed using a random forest (RF) model, and the dataset is segmented location wise and the mean, mode, standard deviation, median, kurtosis, and skewness of time series datasets are examined. The mean of the parameters is adjusted with the coefficient of variation based on the standard values of each parameter. The turbidity in almost all the experimental sites has a normal distribution, with the lowest mean value (0.352 mg/L) and highest (11.9 mg/L) in the Pauri Garhwal and Almora districts, respectively. The pH of the water samples is observed to be in the standard range in all the experimental sites, with average and median values being nearly identical, at 7.189 and 7.20, respectively. However, the pH mode is 0.25. The Cl− concentration varies with mean values from the lowest (0.46 mg/L) to the highest (35.2 mg/L) over the experimental sites, i.e., the Bageshwar and Rudraprayag districts, respectively. Based on the analysis, it was concluded that the water samples were found to be safe to drink and in healthy condition in almost all the districts of the state Uttarakhand, except for the Haridwar district, where some increase in contaminants was observed.

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An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things

Cited by 18 publications

References 67 publications

RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic

RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic

The implication of oversampling on the effectiveness of force signals in the fault detection of endodontic instruments during RCT

Big Data Analysis Framework for Water Quality Indicators with Assimilation of IoT and ML

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