A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods
Most of the sophisticated attacks in the modern age of cybercrime are based, among other things, on specialized phishing campaigns. A challenge in identifying phishing campaigns is defining a classification of patterns that can be generalized and used in different areas and campaigns of a different...
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| Vydáno v: | Applied bionics and biomechanics Ročník 2022; s. 1 - 9 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Egypt
Hindawi
30.05.2022
John Wiley & Sons, Inc Wiley |
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| ISSN: | 1176-2322, 1754-2103 |
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| Abstract | Most of the sophisticated attacks in the modern age of cybercrime are based, among other things, on specialized phishing campaigns. A challenge in identifying phishing campaigns is defining a classification of patterns that can be generalized and used in different areas and campaigns of a different nature. Although efforts have been made to establish a general labeling scheme in their classification, there is still limited data labeled in such a format. The usual approaches are based on feature engineering to correctly identify phishing campaigns, exporting lexical, syntactic, and semantic features, e.g., previous phrases. In this context, the most recent approaches have taken advantage of modern neural network architectures to record hidden information at the phrase and text levels, e.g., Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). However, these models lose semantic information related to the specific problem, resulting in a variation in their performance, depending on the different data sets and the corresponding standards used for labeling. In this paper, we propose to extend word embeddings with word vectors that indicate the semantic similarity of each word with each phishing campaigns template tag. These embedded keywords are calculated based on semantic subfields corresponding to each phishing campaign tag, constructed based on the automatic extraction of keywords representing these tags. Combining general word integrations with vectors is calculated based on word similarity using a set of sequential Kalman filters, which can then power any neural architecture such as LSTM or CNN to predict each phishing campaign. Our experiments use a data indicator to evaluate our approach and achieve remarkable results that reinforce the state-of-the-art. |
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| AbstractList | Most of the sophisticated attacks in the modern age of cybercrime are based, among other things, on specialized phishing campaigns. A challenge in identifying phishing campaigns is defining a classification of patterns that can be generalized and used in different areas and campaigns of a different nature. Although efforts have been made to establish a general labeling scheme in their classification, there is still limited data labeled in such a format. The usual approaches are based on feature engineering to correctly identify phishing campaigns, exporting lexical, syntactic, and semantic features, e.g., previous phrases. In this context, the most recent approaches have taken advantage of modern neural network architectures to record hidden information at the phrase and text levels, e.g., Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). However, these models lose semantic information related to the specific problem, resulting in a variation in their performance, depending on the different data sets and the corresponding standards used for labeling. In this paper, we propose to extend word embeddings with word vectors that indicate the semantic similarity of each word with each phishing campaigns template tag. These embedded keywords are calculated based on semantic subfields corresponding to each phishing campaign tag, constructed based on the automatic extraction of keywords representing these tags. Combining general word integrations with vectors is calculated based on word similarity using a set of sequential Kalman filters, which can then power any neural architecture such as LSTM or CNN to predict each phishing campaign. Our experiments use a data indicator to evaluate our approach and achieve remarkable results that reinforce the state-of-the-art. Most of the sophisticated attacks in the modern age of cybercrime are based, among other things, on specialized phishing campaigns. A challenge in identifying phishing campaigns is defining a classification of patterns that can be generalized and used in different areas and campaigns of a different nature. Although efforts have been made to establish a general labeling scheme in their classification, there is still limited data labeled in such a format. The usual approaches are based on feature engineering to correctly identify phishing campaigns, exporting lexical, syntactic, and semantic features, e.g., previous phrases. In this context, the most recent approaches have taken advantage of modern neural network architectures to record hidden information at the phrase and text levels, e.g., Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). However, these models lose semantic information related to the specific problem, resulting in a variation in their performance, depending on the different data sets and the corresponding standards used for labeling. In this paper, we propose to extend word embeddings with word vectors that indicate the semantic similarity of each word with each phishing campaigns template tag. These embedded keywords are calculated based on semantic subfields corresponding to each phishing campaign tag, constructed based on the automatic extraction of keywords representing these tags. Combining general word integrations with vectors is calculated based on word similarity using a set of sequential Kalman filters, which can then power any neural architecture such as LSTM or CNN to predict each phishing campaign. Our experiments use a data indicator to evaluate our approach and achieve remarkable results that reinforce the state-of-the-art.Most of the sophisticated attacks in the modern age of cybercrime are based, among other things, on specialized phishing campaigns. A challenge in identifying phishing campaigns is defining a classification of patterns that can be generalized and used in different areas and campaigns of a different nature. Although efforts have been made to establish a general labeling scheme in their classification, there is still limited data labeled in such a format. The usual approaches are based on feature engineering to correctly identify phishing campaigns, exporting lexical, syntactic, and semantic features, e.g., previous phrases. In this context, the most recent approaches have taken advantage of modern neural network architectures to record hidden information at the phrase and text levels, e.g., Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). However, these models lose semantic information related to the specific problem, resulting in a variation in their performance, depending on the different data sets and the corresponding standards used for labeling. In this paper, we propose to extend word embeddings with word vectors that indicate the semantic similarity of each word with each phishing campaigns template tag. These embedded keywords are calculated based on semantic subfields corresponding to each phishing campaign tag, constructed based on the automatic extraction of keywords representing these tags. Combining general word integrations with vectors is calculated based on word similarity using a set of sequential Kalman filters, which can then power any neural architecture such as LSTM or CNN to predict each phishing campaign. Our experiments use a data indicator to evaluate our approach and achieve remarkable results that reinforce the state-of-the-art. |
| Audience | Academic |
| Author | Wu, Fei Tang, Yonghui |
| AuthorAffiliation | 1 Shaoyang University, Shaoyang 422000, China 2 Hunan Institute of Engineering, Xiangtan 411101, China |
| AuthorAffiliation_xml | – name: 2 Hunan Institute of Engineering, Xiangtan 411101, China – name: 1 Shaoyang University, Shaoyang 422000, China |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35677198$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/978-3-319-73618-1_3 10.1109/NLPKE.2010.5587788 10.1109/ICITISEE48480.2019.9003803 10.1109/ESCI50559.2021.9396969 10.1109/TASLP.2020.2991544 10.1109/ICCICC46617.2019.9146027 10.1109/ICITEE53064.2021.9611880 10.1109/ICETAS.2018.8629198 10.1109/IACC48062.2019.8971592 10.1109/RAMS48030.2020.9153681 10.1109/EI250167.2020.9347143 10.1007/978-3-319-17091-6_17 10.1109/iSAI-NLP.2018.8692973 10.1109/ASRU.2013.6707745 10.1109/CCST.2019.8888416 10.1109/BigComp51126.2021.00010 10.1186/s40537-021-00444-8 10.1109/CyberSecurity49315.2020.9138871 10.23919/MIPRO.2019.8757074 10.1093/lpr/mgi008 10.1109/ICPCI.2012.6486479 10.1109/ICSCN.2017.8085731 10.1109/ACMI53878.2021.9528204 10.1109/IICSPI.2018.8690387 10.1109/ICEngTechnol.2017.8308186 10.1109/iSAI-NLP.2018.8692959 10.17632/c2gw7fy2j4.2 10.1109/NLPKE.2010.5587778 10.1109/ISAMSR.2018.8540555 10.1109/SMART-TECH49988.2020.00026 10.1007/978-981-10-5780-9_2 10.1007/s11235-020-00733-2 10.1109/ICSE-Companion52605.2021.00137 10.1109/IC4ME247184.2019.9036670 10.1109/PAAP.2014.38 10.1109/CSCI49370.2019.00071 10.1109/ICIRCA48905.2020.9183355 10.3389/frai.2020.00004 10.1109/ICPICS50287.2020.9202191 10.1109/IWECAI50956.2020.00027 10.1109/ICINIS.2015.35 10.3233/ICA-210657 10.1093/lpr/mgm014 10.1093/lawprj/3.3-4.243 10.1007/978-3-319-57358-8_7 |
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| Copyright | Copyright © 2022 Yonghui Tang and Fei Wu. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Yonghui Tang and Fei Wu. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2022 Yonghui Tang and Fei Wu. 2022 |
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| References | 22 44 23 45 24 46 25 47 26 48 27 28 R. G. Krishnan (33) 30 31 10 32 11 12 34 13 35 14 36 15 37 16 38 17 39 18 1 2 3 4 5 6 A. Bhowmick (29) 7 8 9 K. O’Shea (19) 40 41 20 42 21 43 38152735 - Appl Bionics Biomech. 2023 Dec 20;2023:9807027 |
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| SubjectTerms | Algorithms Analysis Artificial intelligence Artificial neural networks Assaults Automation Big Data Classification Cloning Communication Computer architecture Crime Cybercrime Deep learning Efficiency Forecasts and trends Hackers Identity theft Kalman filters Labeling Long short-term memory Machine learning Methods Natural language Neural networks Phishing Semantics Similarity Social networks |
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| Title | A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods |
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