FMixCutMatch for semi-supervised deep learning

Wei, Xiang; Kong, Xiangyuan; Lu, Siyang; Xing, Weiwei; Lü, Wei

doi:10.1016/j.neunet.2020.10.018

Cited by 21 publications

(7 citation statements)

References 13 publications

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“…This process continues for several iterations and better prediction results are achieved at the end of the process. Recently, different semi-supervised deep learning algorithms were developed and applied to different problems [ 60 , 78 , 86 , P29 ]. In addition, unsupervised deep learning algorithms can help to reduce the burden of labeled data.…”

Section: Resultsmentioning

confidence: 99%

“…They applied URL-based, domain-based, path-based, file-based, and query-based features. Wei [ 78 , P28 ] used an embedding layer, three convolutional layers, three max pooling layers, one fully-connected layer to develop the CNN-based phishing detection model. Li et al [ 42 , P13 , P14 ] applied a convolutional later of four 3*1 filters, a max pooling layer, two dense layers, and a softmax output layer for their model.…”

Section: Resultsmentioning

confidence: 99%

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Applications of deep learning for phishing detection: a systematic literature review

Çatal

Giray²,

Tekinerdoğan

et al. 2022

Knowl Inf Syst

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Phishing attacks aim to steal confidential information using sophisticated methods, techniques, and tools such as phishing through content injection, social engineering, online social networks, and mobile applications. To avoid and mitigate the risks of these attacks, several phishing detection approaches were developed, among which deep learning algorithms provided promising results. However, the results and the corresponding lessons learned are fragmented over many different studies and there is a lack of a systematic overview of the use of deep learning algorithms in phishing detection. Hence, we performed a systematic literature review (SLR) to identify, assess, and synthesize the results on deep learning approaches for phishing detection as reported by the selected scientific publications. We address nine research questions and provide an overview of how deep learning algorithms have been used for phishing detection from several aspects. In total, 43 journal articles were selected from electronic databases to derive the answers for the defined research questions. Our SLR study shows that except for one study, all the provided models applied supervised deep learning algorithms. The widely used data sources were URL-related data, third party information on the website, website content-related data, and email. The most used deep learning algorithms were deep neural networks (DNN), convolutional neural networks, and recurrent neural networks/long short-term memory networks. DNN and hybrid deep learning algorithms provided the best performance among other deep learning-based algorithms. 72% of the studies did not apply any feature selection algorithm to build the prediction model. PhishTank was the most used dataset among other datasets. While Keras and Tensorflow were the most preferred deep learning frameworks, 46% of the articles did not mention any framework. This study also highlights several challenges for phishing detection to pave the way for further research.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Applications of deep learning for phishing detection: a systematic literature review

Çatal

Giray²,

Tekinerdoğan

et al. 2022

Knowl Inf Syst

View full text Add to dashboard Cite

show abstract

“…These modelling assumptions create a paradigm where unlabelled data can be useful for model estimation (Chapelle et al, 2010). There has been a recent revival of interest in semi-supervised learning in the machine learning community due to impressive empirical progress on benchmark image and text classification datasets; for example, Tarvainen and Valpola (2017), Laine and Aila (2017), Miyato et al (2019), Berthelot et al (2019), Xie et al (2019), Berthelot et al (2020), Sohn et al (2020), Wei et al (2021).…”

Section: Brief Overview Of Ssl Approachesmentioning

confidence: 99%

“…Additional unlabelled observations are generated by perturbing the original set of unlabelled observations by adding random noise or transformations such as rotations or translations. Data augmentation is typically coupled with consistency regularization so that similar predictions are encouraged on the original instances and the augmented versions (Berthelot et al, 2019;Nair et al, 2019;Wei et al, 2021). The combined use of small local alterations and more aggressive global changes has been found to be an effective strategy (Sohn et al, 2020).…”

Section: Brief Overview Of Ssl Approachesmentioning

confidence: 99%

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Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review

Ahfock¹,

McLachlan²

2021

Preprint

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There has been increasing attention to semi-supervised learning (SSL) approaches in machine learning to forming a classifier in situations where the training data for a classifier consists of a limited number of classified observations but a much larger number of unclassified observations. This is because the procurement of classified data can be quite costly due to high acquisition costs and subsequent financial, time, and ethical issues that can arise in attempts to provide the true class labels for the unclassified data that have been acquired. We provide here a review of statistical SSL approaches to this problem, focussing on the recent result that a classifier formed from a partially classified sample can actually have smaller expected error rate than that if the sample were completely classified. This rather paradoxical outcome is able to be achieved by introducing a framework with a missingness mechanism for the missing labels of the unclassified observations. It is most relevant in commonly occurring situations in practice, where the unclassified data occur primarily in regions of relatively high entropy in the feature space thereby making it difficult for their class labels to be easily obtained.

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AMRE: An Attention-Based CRNN for Manchu Word Recognition on a Woodblock-Printed Dataset

Wang

Lu²,

Wang³

et al. 2023

Lecture Notes in Computer Science

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FMixCutMatch for semi-supervised deep learning

Cited by 21 publications

References 13 publications

Applications of deep learning for phishing detection: a systematic literature review

Applications of deep learning for phishing detection: a systematic literature review

Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review

AMRE: An Attention-Based CRNN for Manchu Word Recognition on a Woodblock-Printed Dataset

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