Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India

Khurana, Madhu; Saxena, Vikas

doi:10.1016/j.jksuci.2018.09.008

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…For example, Neural Networks (NNs) [35,36] and CNN [37][38][39][40] can automatically learn features from raw traffic data of devices, eliminating manual feature selection. However, a single DL model may not fit all datasets, so ensemble learning models [41] have been proposed and have obtained higher classification accuracy than the individual model [42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

WYSIWYG: IoT Device Identification Based on WebUI Login Pages

Wang¹,

Li²,

Jing³

et al. 2022

Sensors

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With the improvement of intelligence and interconnection, Internet of Things (IoT) devices tend to become more vulnerable and exposed to many threats. Device identification is the foundation of many cybersecurity operations, such as asset management, vulnerability reaction, and situational awareness, which are important for enhancing the security of IoT devices. The more information sources and the more angles of view we have, the more precise identification results we obtain. This study proposes a novel and alternative method for IoT device identification, which introduces commonly available WebUI login pages with distinctive characteristics specific to vendors as the data source and uses an ensemble learning model based on a combination of Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) for device vendor identification and develops an Optical Character Recognition (OCR) based method for device type and model identification. The experimental results show that the ensemble learning model can achieve 99.1% accuracy and 99.5% F1-Score in the determination of whether a device is from a vendor that appeared in the training dataset, and if the answer is positive, 98% accuracy and 98.3% F1-Score in identifying which vendor it is from. The OCR-based method can identify fine-grained attributes of the device and achieve an accuracy of 99.46% in device model identification, which is higher than the results of the Shodan cyber search engine by a considerable margin of 11.39%.

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Section: Introductionmentioning

confidence: 99%

WYSIWYG: IoT Device Identification Based on WebUI Login Pages

Wang¹,

Li²,

Jing³

et al. 2022

Sensors

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show abstract

“…The general framework of integrated learning is mainly composed of base learning devices. In the data set, the base learning devices are assigned training data sets through different rules, allowing each base learning device to perform algorithm operations, and then performing algorithm fusion integration according to the fusion integration rules; that is, forming an integrated model to get a strong learning machine [ 24 ]. In the integrated generation, there are different sampling rules, including uniform sampling, systematic sampling and weighted sampling like boosting [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Research on a Gas Concentration Prediction Algorithm Based on Stacking

Meng

Zhao

2021

Sensors

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Machine learning algorithms play an important role in the detection of toxic, flammable and explosive gases, and they are extremely important for the study of mixed gas classification and concentration prediction methods. To solve the problem of low prediction accuracy of gas concentration regression prediction algorithms, a gas concentration prediction algorithm based on a stacking model is proposed in the current research. In this paper, the stochastic forest, extreme random regression tree and gradient boosting decision tree (GBDT) regression algorithms are selected as the base learning devices and use the stacking algorithm to take the output of each base learning device as input to train a new model to produce a final output. Through the stacking model, the grid search algorithm is studied to automatically optimize the parameters so that the performance of the entire system can reach the optimal parameters. Through experimental simulation, the gas concentration prediction algorithm based on stacking model has better prediction effect than other integrated frame algorithms and the accuracy of mixed gas concentration prediction is improved.

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