Accurate and fast URL phishing detector: A convolutional neural network approach

Wei, Wei; Ke, Qiao; Nowak, Jakub; Korytkowski, Marcin; Scherer, Rafał; Woźniak, Marcin

doi:10.1016/j.comnet.2020.107275

Cited by 148 publications

(95 citation statements)

References 16 publications

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“…As shown in Table Ⅲ, all methods had a good performance. Wei et al [32] also utilized character embedding technology. They utilized the character embedding sequence of the URL as the input of the CNN network to obtain a reasonable accuracy.…”

Section: F Comparison With Methodsmentioning

confidence: 99%

Malicious URL Detection Based on a Parallel Neural Joint Model

et al. 2021

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Section: F Comparison With Methodsmentioning

confidence: 99%

Malicious URL Detection Based on a Parallel Neural Joint Model

et al. 2021

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“…For the 200 learner knowledge-space models obtained from the simulation, use the contribution-based learning target selection algorithm and the learning cost-based learning target selection algorithm to obtain the corresponding sets of learning target selection results; for each learning target obtained in the previous step, use the domain knowledge ontology to obtain the set of knowledge points that learners of the current knowledge space model need to learn and have not yet mastered to achieve the learning target [19][20][21][22]. For the set of knowledge points obtained in the previous step, the bottom-up knowledge point-learning sequence-planning algorithm is used to obtain the knowledge point-learning sequence-planning results for learners with the sequential learning style, and the left-to-right knowledge point-learning sequence-planning algorithm is used to obtain the knowledge point-learning sequenceplanning results for learners with the comprehensive learning style.…”

Section: English Fragmented Reading-learning Applicationsmentioning

confidence: 99%

Adaptive Algorithm Recommendation and Application of Learning Resources in English Fragmented Reading

Cheng

Wang

2021

Complexity

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This paper firstly designs a five-dimensional model of learners’ characteristics (learners’ English reading ability, cognitive style, learning goal, learning situation, and learning effect) and a three-dimensional model of English reading resources’ characteristics (question types, topics, and difficulty of resources) in a fragmented learning environment through literature research. At the same time, to make the learning resources meet the characteristics of fragmented learning time and space, the English Level 4 reading resources are reasonably designed and segmented to adapt to the needs of learners’ mobile fragmented learning. Then, combined with machine learning algorithms, an adaptive recommendation model of learning resources in English fragmented reading is constructed. The algorithm-based adaptive recommendation algorithm for English fragmented reading resources is designed. Based on the generated decision trees, the expression rules are parsed to achieve adaptive pushing of resources. The results of this study show that adaptive recommendation of learning resources in English fragmented reading can help teachers to develop future resource recommendation strategies through effective data collection to adaptively push resources that are close to learners’ individual needs. The use of mobile by English learners to learn to read in a fragmented learning context enables targeted training in weak areas of English reading, thus enhancing different aspects of learners’ reading skills.

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“…In terms of the triple bottom line theory of corporate social responsibility, the traditional corporate social responsibility mainly reflects the improvement of profits, the dividends from shareholders and taxpayers, the environmental protection, and other social stakeholders [24][25][26][27]. From the perspective of corporate social responsibility's own value or mechanism, corporate social responsibility not only creates higher value for stakeholders or external social environment, but also creates corresponding value for the enterprise itself [28].…”

Section: Operation Mechanism Of Low-carbon Circular Economy Development Systemmentioning

confidence: 99%

Corporate Social Responsibility Based on Radial Basis Function Neural Network Evaluation Model of Low‐Carbon Circular Economy Coupled Development

Gong¹,

Guo

2021

Complexity

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Under the background that the development of low-carbon circular economy is the objective requirement for the in-depth implementation of scientific development and the inevitable choice for promoting the sustainable development of economy and society, it is not only the requirement of corporate social responsibility but also the path to realize corporate social responsibility. Enterprises should become the representative and model of social responsibility practice in the development of low-carbon circular economy, in order to promote the fulfilment and development of corporate social responsibility in the whole society. Therefore, it is of great theoretical and practical significance to study the realization of corporate social responsibility in the context of low-carbon circular economy. This paper introduces the connotation of low-carbon circular economy and corporate social responsibility, analyses the reality and theoretical basis of realizing corporate social responsibility in low-carbon circular economy, analyses the interactive relationship between the development of low-carbon circular economy and the realization of corporate social responsibility, and puts forward the construction of enterprise low-carbon operation mechanism. This paper uses the research of corporate social responsibility based on radial basis function neural network to build a low-carbon circular economy. The evaluation model of environment economy coupling development is verified by an example, which provides useful guidance for the evaluation and development of corporate social responsibility.

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Accurate and fast URL phishing detector: A convolutional neural network approach

Cited by 148 publications

References 16 publications

Malicious URL Detection Based on a Parallel Neural Joint Model

Malicious URL Detection Based on a Parallel Neural Joint Model

Adaptive Algorithm Recommendation and Application of Learning Resources in English Fragmented Reading

Corporate Social Responsibility Based on Radial Basis Function Neural Network Evaluation Model of Low‐Carbon Circular Economy Coupled Development

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