Improving malware detection using big data and ensemble learning

Gupta, Deepak; Rani, Rinkle

doi:10.1016/j.compeleceng.2020.106729

Cited by 72 publications

(35 citation statements)

References 10 publications

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“…Although the effectiveness of malware detection using ensemble classifiers is very promising, several researchers note that the memory and processing requirements make large ensemble classifiers unsuitable for malware detection in big data environments [178]. To address this problem, a pruning method has been recently proposed [179], as well as a novel method of selecting optimal classifiers based on weighted voting [180].…”

Section: E Ensemble Classifiersmentioning

confidence: 99%

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Caviglione

Choraś

Corona³

et al. 2021

IEEE Access

100

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Section: E Ensemble Classifiersmentioning

confidence: 99%

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Caviglione

Choraś

Corona³

et al. 2021

IEEE Access

100

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“…Experimental results prove the best performance of extreme gradient boosting over other ensemble models and machine learning models. For malware detection (Gupta & Rani, 2020) used five base-predictors, and the output of each base-predictor was ranked by calculating and aggregating the output weights. Then using two ensemble techniques Voting and Stacking to rank the output.…”

Section: Related Workmentioning

confidence: 99%

“…It is known as the most efficient technique for improving the performance of machine learning models. Nowadays, ensemble learning methods are gaining more popularity than traditional individual machine learning models in numerous classification tasks like fake news detection (Kaur, Kumar & Kumaraguru, 2020), malware detection (Gupta & Rani, 2020). Ensemble learning methods fall into two categories: parallel ensemble and sequential ensemble.…”

Section: Ensemble Learning Modelsmentioning

confidence: 99%

“…This can be achieved in numerous ways, including homogenous models with diverse parameters, heterogeneous models, resampling the training corpus, or using different methods to combine predictions of base-predictors (Gupta & Rani, 2020). Ensemble learning can be of two types: parallel and sequential.…”

Section: Introductionmentioning

confidence: 99%

“…In the sequential ensemble, basepredictors are trained sequentially, where a model attempts to correct its predecessor (Pham et al, 2021). Ensemble learning methods have shown good performance in various applications, including solar irradiance prediction (Lee et al, 2020), slope stability analysis (Pham et al, 2021), natural language processing (Sangamnerkar et al, 2020), malware detection (Gupta & Rani, 2020), traffic incident detection (Xiao, 2019). In the past, several studies explored machine learning models for fake news detection task in a few languages like Portuguese (Monteiro et al, 2018;Silva et al, 2020), Spanish (Posadas-Durán et al, 2019;Abonizio et al, 2020), Urdu , Arabic (Alkhair et al, 2019), Slavic (Faustini & Covões, 2020;Kapusta & Obonya, 2020), and English (Kaur, Kumar & Kumaraguru, 2020;Ozbay & Alatas, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media

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The popularity of the internet, smartphones, and social networks has contributed to the proliferation of misleading information like fake news and fake reviews on news blogs, online newspapers, and e-commerce applications. Fake news has a worldwide impact and potential to change political scenarios, deceive people into increasing product sales, defaming politicians or celebrities, and misguiding visitors to stop visiting a place or country. Therefore, it is vital to find automatic methods to detect fake news online. In several past studies, the focus was the English language, but the resource-poor languages have been completely ignored because of the scarcity of labeled corpus. In this study, we investigate this issue in the Urdu language. Our contribution is threefold. First, we design an annotated corpus of Urdu news articles for the fake news detection tasks. Second, we explore three individual machine learning models to detect fake news. Third, we use five ensemble learning methods to ensemble the base-predictors’ predictions to improve the fake news detection system’s overall performance. Our experiment results on two Urdu news corpora show the superiority of ensemble models over individual machine learning models. Three performance metrics balanced accuracy, the area under the curve, and mean absolute error used to find that Ensemble Selection and Vote models outperform the other machine learning and ensemble learning models.

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Cataract grading method based on deep convolutional neural networks and stacking ensemble learning

Elloumi

2022

Int J Imaging Syst Tech

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The cataract is the most common cause of severe vision impairment or blindness worldwide. A periodical diagnosis is recommended in order to prevent cataract severity, where screening might be feasibly ensured through fundus images. In this paper, we propose a CAD system to efficiently grade the cataract from fundus images. For such a need, an ensemble learning framework is put forward where the knowledge of three convolutional deep neural networks is stacked in order to perform an efficient cataract prediction. The main contributions of this work are given as follows: (1) Preprocessing and data augmentation of fundus images are carried out to ensure the robustness of the cataract grading; (2) The well‐known DL architectures (Inception‐V3, MobileNet‐V2 and NasNet‐Mobile) are fine‐tuned and learned as base classifiers; (3) An Extreme Learning Machine (ELM) is performed as a meta classifier to stack the predictions of base classifiers. The evaluation is conducted using a dataset of 590 fundus images selected from two public databases. The suggested framework achieves 94.07% accuracy, 94.37% sensitivity, 93.62% specificity, 95.71% precision and 95.04% F‐measure for cataract grading. The experimentation proves that the proposed framework successfully grades fundus images into cataract severity. Moreover, stacking ensemble learning allows achieving a performance that significantly surpasses the ones realized by each DL architecture, applied separately.

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Improving malware detection using big data and ensemble learning

Cited by 72 publications

References 10 publications

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media

Cataract grading method based on deep convolutional neural networks and stacking ensemble learning

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