Multimodal framework for phishing attack detection and mitigation through behavior analysis using EM-BERT and SPCA-BASED EAI-SC-LSTM
IntroductionThe rapid growth of advanced networking causes a significant increase in malicious threats to website data for accessing user information via phishing attacks. For the detection of phishing attacks, many works are developed based on a single data source. But, detecting the phishing attac...
Saved in:
| Published in: | Frontiers in communications and networks Vol. 6 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Frontiers Media S.A
08.07.2025
|
| Subjects: | |
| ISSN: | 2673-530X, 2673-530X |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | IntroductionThe rapid growth of advanced networking causes a significant increase in malicious threats to website data for accessing user information via phishing attacks. For the detection of phishing attacks, many works are developed based on a single data source. But, detecting the phishing attacks of different web sources was not concentrated in any of the existing works. Thus, multiple data sources, including SMS, E-Mail, and URL links, are used in this paper to detect and mitigate phishing attacks.MethodsInitially, the input data is collected from the SMS, E-Mail, and URL datasets. The contents and URLs are extracted from the datasets. Next, the textual analysis, including behavioral analysis and structural analysis, is carried out on the extracted URL. Moreover, by utilizing the Entropy Macqueen-based Bidirectional Encoder Representations from Transformers (EM-BERT) algorithm, the contents extracted from SMS and E-Mail datasets and the textually analyzed characters of the URL are transformed into vector form. Simultaneously, the CSS files and images are obtained from the URL dataset. Then, by utilizing Spherical Principal Component Analysis (SPCA), the features are extracted. Further, the optimal features are chosen by using the Cauchy distribution-based Seagull Optimization Algorithm (CSOA). Next, the phishing attack is detected using the Explainable AI SERF CoLU Long Short Term Memory (EAI-SC-LSTM) model. The recognized phishing data and URL are updated to the Blacklist; hence, any new URL, which is already on Blacklist, is reported to the user.ResultsAs per the experimental outcomes, the proposed EAI-SC-LSTM attains accuracies of 99.627% for SSC, 99.645% for PEC, and 99.541% for WPD in phishing attack detection, which are higher than the existing works. Moreover, the proposed technique detects the phishing attack within a training time of 24417 ms (PEC Dataset).DiscussionThus, cybersecurity is improved against the evolving phishing threats. |
|---|---|
| AbstractList | IntroductionThe rapid growth of advanced networking causes a significant increase in malicious threats to website data for accessing user information via phishing attacks. For the detection of phishing attacks, many works are developed based on a single data source. But, detecting the phishing attacks of different web sources was not concentrated in any of the existing works. Thus, multiple data sources, including SMS, E-Mail, and URL links, are used in this paper to detect and mitigate phishing attacks.MethodsInitially, the input data is collected from the SMS, E-Mail, and URL datasets. The contents and URLs are extracted from the datasets. Next, the textual analysis, including behavioral analysis and structural analysis, is carried out on the extracted URL. Moreover, by utilizing the Entropy Macqueen-based Bidirectional Encoder Representations from Transformers (EM-BERT) algorithm, the contents extracted from SMS and E-Mail datasets and the textually analyzed characters of the URL are transformed into vector form. Simultaneously, the CSS files and images are obtained from the URL dataset. Then, by utilizing Spherical Principal Component Analysis (SPCA), the features are extracted. Further, the optimal features are chosen by using the Cauchy distribution-based Seagull Optimization Algorithm (CSOA). Next, the phishing attack is detected using the Explainable AI SERF CoLU Long Short Term Memory (EAI-SC-LSTM) model. The recognized phishing data and URL are updated to the Blacklist; hence, any new URL, which is already on Blacklist, is reported to the user.ResultsAs per the experimental outcomes, the proposed EAI-SC-LSTM attains accuracies of 99.627% for SSC, 99.645% for PEC, and 99.541% for WPD in phishing attack detection, which are higher than the existing works. Moreover, the proposed technique detects the phishing attack within a training time of 24417 ms (PEC Dataset).DiscussionThus, cybersecurity is improved against the evolving phishing threats. |
| Author | Murhej, Mahmoud Nallasivan, G. |
| Author_xml | – sequence: 1 givenname: Mahmoud surname: Murhej fullname: Murhej, Mahmoud – sequence: 2 givenname: G. surname: Nallasivan fullname: Nallasivan, G. |
| BookMark | eNpNkctuEzEUQC1UJErpD7DyD0zwY_yYZRoGiJQIRILEzroztjNuZ8aV7YC658NJ0gqxug_pnM15i67mODuE3lOy4Fw3H3zqp3nBCBMLKrSSon6FrplUvBKc_Lz6b3-DbnO-J4QwpWvayGv0Z3scS5iihRH7BJP7HdMD9jHhxyHkIcwHDKVA_4CtK64vIc4YZounUMIBLmcZUjweBty5AX6FEwkzjE85ZHzMZ77dVnft9_0F231bLau75a79iNvlutqtqs1uv32HXnsYs7t9mTfox6d2v_pSbb5-Xq-Wm6rnlJRKWaqUkl5wDaRrNKkJNNpRDlYCBWuJ1op2tey190x5QWwnnHROCyY17fgNWj97bYR785jCBOnJRAjm8ojpYCCV0I_OeEpPSA1Sia5WVmvr64YK7xWTPQh2crFnV59izsn5fz5KzDmLuWQx5yzmJQv_C9_dguo |
| Cites_doi | 10.1016/j.procs.2021.05.077 10.1109/ACCESS.2023.3237798 10.1016/j.heliyon.2021.e07437 10.1016/j.comcom.2021.04.023 10.1109/ACCESS.2023.3293063 10.3390/electronics12010042 10.1016/j.cose.2023.103387 10.1109/ACCESS.2022.3223111 10.1109/ACCESS.2022.3168235 10.1038/s41598-022-10841-5 10.1109/ACCESS.2024.3351946 10.1016/j.dsm.2024.08.004 10.1016/j.dss.2023.114102 10.3390/electronics12214545 10.1016/j.cose.2024.103736 10.1109/ACCESS.2022.3166474 10.1016/j.cose.2021.102421 10.1016/j.eswa.2022.118010 10.3390/make3030034 10.1007/s10207-024-00851-x 10.1109/ACCESS.2023.3252366 10.1109/ACCESS.2022.3183083 10.1109/ACCESS.2021.3137636 10.1016/j.engappai.2021.104347 10.1109/ACCESS.2024.3463871 10.1016/j.jksuci.2023.01.004 10.1007/s11235-020-00733-2 10.1016/j.eswa.2023.121183 10.1016/j.csa.2024.100036 10.3390/electronics11071090 10.1109/ACCESS.2024.3352629 10.1016/j.cose.2024.104129 10.1016/j.aej.2024.09.115 10.1109/ACCESS.2022.3196018 10.3390/sym16020248 10.1007/s10115-022-01672-x 10.1016/j.eswa.2020.113863 10.1007/s00521-020-05354-z 10.1016/j.aej.2024.06.070 10.1007/s12046024025820 10.1109/ACCESS.2025.3525998 |
| ContentType | Journal Article |
| DBID | AAYXX CITATION DOA |
| DOI | 10.3389/frcmn.2025.1587654 |
| DatabaseName | CrossRef DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2673-530X |
| ExternalDocumentID | oai_doaj_org_article_f116814a675b47d88df4915ff726ca52 10_3389_frcmn_2025_1587654 |
| GroupedDBID | 9T4 AAFWJ AAYXX AFPKN ALMA_UNASSIGNED_HOLDINGS CITATION GROUPED_DOAJ M~E OK1 |
| ID | FETCH-LOGICAL-c310t-7d17776f538a0b98040a98e13ad6a1add08871b46c8ff27f50db5e6ee852681b3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001533300700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2673-530X |
| IngestDate | Fri Oct 03 12:42:39 EDT 2025 Sat Nov 29 07:45:51 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c310t-7d17776f538a0b98040a98e13ad6a1add08871b46c8ff27f50db5e6ee852681b3 |
| OpenAccessLink | https://doaj.org/article/f116814a675b47d88df4915ff726ca52 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_f116814a675b47d88df4915ff726ca52 crossref_primary_10_3389_frcmn_2025_1587654 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-07-08 |
| PublicationDateYYYYMMDD | 2025-07-08 |
| PublicationDate_xml | – month: 07 year: 2025 text: 2025-07-08 day: 08 |
| PublicationDecade | 2020 |
| PublicationTitle | Frontiers in communications and networks |
| PublicationYear | 2025 |
| Publisher | Frontiers Media S.A |
| Publisher_xml | – name: Frontiers Media S.A |
| References | Asiri (B9) 2023; 11 Gupta (B22) 2021; 175 Sturman (B42) 2024; 148 Alotaibi (B6) 2025; 110 Al-Ahmadi (B3) 2022; 10 Karim (B25) 2023; 11 Basit (B13) 2021; 76 Catal (B18) 2022; 64 Mehmood (B29) 2024; 12 Hannousse (B23) 2021; 104 Shombot (B41) 2024; 2 Sudar (B43) 2024; 49 Thakur (B46) 2023; 12 Akour (B2) 2021; 24 Aljabri (B4) 2024; 106 Elberri (B21) 2024; 23 Yang (B48) 2021; 165 Naqvi (B30) 2023; 132 Aljofey (B5) 2022; 12 Bhagwat (B14) 2021 Champa (B20) Rashid (B34) 2023; 11 Salloum (B37) 2021; 189 Safi (B35) 2023; 35 Bu (B17) 2022; 11 Ariyadasa (B8) 2022; 10 Champa (B19) Tang (B45) 2022; 10 Opara (B32) 2024; 236 Tang (B44) 2021; 3 Shafin (B40) 2024; 8 van Geest (B47) 2024; 139 Mahmoud (B28) 2013; 5 Rao (B33) 2021; 33 Sahingoz (B36) 2024; 12 Sanchez-Paniagua (B39) 2022; 207 Aslam (B10) 2024; 16 Biswas (B15) 2024; 177 Kara (B24) 2022; 10 Brezeanu (B16) 2025; 13 Atlam (B11) 2023; 12 Liu (B27) 2021; 110 Alsubaei (B7) 2024; 12 Odeh (B31) 2021 Salloum (B38) 2022; 10 Li (B26) 2023; 11 Abdillah (B1) 2022; 10 Balogun (B12) 2021; 7 |
| References_xml | – start-page: 1 volume-title: 2024 12th international symposium on digital forensics and security (ISDFS) ident: B19 article-title: Why phishing emails escape detection: a closer look at the failure points – start-page: 1 ident: B20 article-title: Curated datasets and feature analysis for phishing email detection with machine learning – volume: 189 start-page: 19 year: 2021 ident: B37 article-title: Phishing email detection using Natural Language Processing techniques: a literature survey publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2021.05.077 – volume: 11 start-page: 6421 year: 2023 ident: B9 article-title: A survey of intelligent detection designs of HTML URL phishing attacks publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3237798 – volume: 7 start-page: e07437 year: 2021 ident: B12 article-title: Improving the phishing website detection using empirical analysis of Function Tree and its variants publication-title: Heliyon doi: 10.1016/j.heliyon.2021.e07437 – volume: 175 start-page: 47 year: 2021 ident: B22 article-title: A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment publication-title: Comput. Commun. doi: 10.1016/j.comcom.2021.04.023 – volume: 11 start-page: 71925 year: 2023 ident: B26 article-title: Uncovering the cloak: a systematic review of techniques used to conceal phishing websites publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3293063 – volume: 5 start-page: 698 year: 2013 ident: B28 article-title: A framework for an E-learning system based on semantic web publication-title: Int. J. Comput. Sci. Eng. – volume: 12 start-page: 42 year: 2023 ident: B11 article-title: Business email compromise phishing detection based on machine learning: a systematic literature review publication-title: Electron. Switz. doi: 10.3390/electronics12010042 – volume: 132 start-page: 103387 year: 2023 ident: B30 article-title: Mitigation strategies against the phishing attacks: a systematic literature review publication-title: Comput. Secur. doi: 10.1016/j.cose.2023.103387 – volume: 10 start-page: 124420 year: 2022 ident: B24 article-title: Characteristics of understanding URLs and domain names features: the detection of phishing websites with machine learning methods publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3223111 – volume: 10 start-page: 42459 year: 2022 ident: B3 article-title: PDGAN: phishing detection with generative adversarial networks publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3168235 – volume: 12 start-page: 8842 year: 2022 ident: B5 article-title: An effective detection approach for phishing websites using URL and HTML features publication-title: Sci. Rep. doi: 10.1038/s41598-022-10841-5 – volume: 12 start-page: 8373 year: 2024 ident: B7 article-title: Enhancing phishing detection: a novel hybrid deep learning framework for cybercrime forensics publication-title: IEEE Access doi: 10.1109/ACCESS.2024.3351946 – volume: 8 start-page: 127 year: 2024 ident: B40 article-title: An explainable feature selection framework for web phishing detection with machine learning publication-title: Data Sci. Manag. doi: 10.1016/j.dsm.2024.08.004 – volume: 177 start-page: 114102 year: 2024 ident: B15 article-title: A hybrid framework using explainable AI (XAI) in cyber-risk management for defence and recovery against phishing attacks publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2023.114102 – volume: 12 start-page: 4545 year: 2023 ident: B46 article-title: A systematic review on deep-learning-based phishing email detection publication-title: Electron. Switz. doi: 10.3390/electronics12214545 – volume: 139 start-page: 103736 year: 2024 ident: B47 article-title: The applicability of a hybrid framework for automated phishing detection publication-title: Comput. Secur. doi: 10.1016/j.cose.2024.103736 – volume: 10 start-page: 41574 year: 2022 ident: B1 article-title: Phishing classification techniques: a systematic literature review publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3166474 – volume: 110 start-page: 102421 year: 2021 ident: B27 article-title: An efficient multistage phishing website detection model based on the CASE feature framework: aiming at the real web environment publication-title: Comput. Secur. doi: 10.1016/j.cose.2021.102421 – volume: 207 start-page: 118010 year: 2022 ident: B39 article-title: Phishing websites detection using a novel multipurpose dataset and web technologies features publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.118010 – volume: 3 start-page: 672 year: 2021 ident: B44 article-title: A survey of machine learning-based solutions for phishing website detection publication-title: Mach. Learn. Knowl. Extr. doi: 10.3390/make3030034 – volume: 24 start-page: 1 year: 2021 ident: B2 article-title: Using Classical Machine Learning for phishing websites detection form URLS publication-title: J. Manag. Inf. Decis. Sci. – volume: 23 start-page: 2583 year: 2024 ident: B21 article-title: A cyber defense system against phishing attacks with deep learning game theory and LSTM-CNN with African vulture optimization algorithm (AVOA) publication-title: Int. J. Inf. Secur. doi: 10.1007/s10207-024-00851-x – volume: 11 start-page: 36805 year: 2023 ident: B25 article-title: Phishing detection system through hybrid machine learning based on URL publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3252366 – volume: 10 start-page: 65703 year: 2022 ident: B38 article-title: A systematic literature review on phishing email detection using Natural Language Processing techniques publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3183083 – volume: 10 start-page: 1509 year: 2022 ident: B45 article-title: A deep learning-based framework for phishing website detection publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3137636 – volume: 104 start-page: 104347 year: 2021 ident: B23 article-title: Towards benchmark datasets for machine learning based website phishing detection: an experimental study publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2021.104347 – volume: 12 start-page: 137176 year: 2024 ident: B29 article-title: Enhancing smishing detection: a deep learning approach for improved accuracy and reduced False positives publication-title: IEEE Access doi: 10.1109/ACCESS.2024.3463871 – volume: 35 start-page: 590 year: 2023 ident: B35 article-title: A systematic literature review on phishing website detection techniques publication-title: J. King Saud Univ. - Comput. Inf. Sci. doi: 10.1016/j.jksuci.2023.01.004 – volume: 76 start-page: 139 year: 2021 ident: B13 article-title: A comprehensive survey of AI-enabled phishing attacks detection techniques publication-title: Telecommun. Syst. doi: 10.1007/s11235-020-00733-2 – volume: 236 start-page: 121183 year: 2024 ident: B32 article-title: Look before you leap: detecting phishing web pages by exploiting raw URL and HTML characteristics publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2023.121183 – volume: 2 start-page: 100036 year: 2024 ident: B41 article-title: An application for predicting phishing attacks: a case of implementing a support vector machine learning model publication-title: Cyber Secur. Appl. doi: 10.1016/j.csa.2024.100036 – volume: 11 start-page: 1090 year: 2022 ident: B17 article-title: Optimized URL feature selection based on genetic-algorithm-embedded deep learning for phishing website detection publication-title: Electron. Switz. doi: 10.3390/electronics11071090 – volume: 11 start-page: 451 year: 2023 ident: B34 article-title: Cloud-based machine learning approach for accurate detection of website phishing publication-title: Int. J. Intell. Syst. Appl. Eng. – volume: 12 start-page: 8052 year: 2024 ident: B36 article-title: Dephides: deep learning based phishing detection system publication-title: IEEE Access doi: 10.1109/ACCESS.2024.3352629 – volume: 148 start-page: 104129 year: 2024 ident: B42 article-title: Security awareness, decision style, knowledge, and phishing email detection: moderated mediation analyses publication-title: Comput. & Secur. doi: 10.1016/j.cose.2024.104129 – volume: 110 start-page: 490 year: 2025 ident: B6 article-title: Explainable artificial intelligence in web phishing classification on secure IoT with cloud-based cyber-physical systems publication-title: Alexandria Eng. J. doi: 10.1016/j.aej.2024.09.115 – volume: 10 start-page: 82355 year: 2022 ident: B8 article-title: Combining long-term recurrent convolutional and graph convolutional networks to detect phishing sites using URL and HTML publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3196018 – volume: 16 start-page: 248 year: 2024 ident: B10 article-title: AntiPhishStack: LSTM-based stacked generalization model for optimized phishing URL detection publication-title: Symmetry doi: 10.3390/sym16020248 – volume: 64 start-page: 1457 year: 2022 ident: B18 article-title: Applications of deep learning for phishing detection: a systematic literature review publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-022-01672-x – volume: 165 start-page: 113863 year: 2021 ident: B48 article-title: An improved ELM-based and data preprocessing integrated approach for phishing detection considering comprehensive features publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2020.113863 – volume: 33 start-page: 5733 year: 2021 ident: B33 article-title: A heuristic technique to detect phishing websites using TWSVM classifier publication-title: Neural Comput. Appl. doi: 10.1007/s00521-020-05354-z – volume: 106 start-page: 164 year: 2024 ident: B4 article-title: Hybrid stacked autoencoder with dwarf mongoose optimization for Phishing attack detection in internet of things environment publication-title: Alexandria Eng. J. doi: 10.1016/j.aej.2024.06.070 – start-page: 0813 year: 2021 ident: B31 article-title: Machine LearningTechniquesfor detection of website phishing: a review for promises and challenges – volume: 49 start-page: 232 year: 2024 ident: B43 article-title: Detection of adversarial phishing attack using machine learning techniques publication-title: Sādhanā doi: 10.1007/s12046024025820 – start-page: 1505 year: 2021 ident: B14 article-title: A methodical overview on detection identification and proactive prevention of phishing websites – volume: 13 start-page: 4460 year: 2025 ident: B16 article-title: Phish fighter: self updating machine learning shield against phishing kits based on HTML code analysis publication-title: IEEE Access doi: 10.1109/ACCESS.2025.3525998 |
| SSID | ssj0002784196 |
| Score | 2.296599 |
| Snippet | IntroductionThe rapid growth of advanced networking causes a significant increase in malicious threats to website data for accessing user information via... |
| SourceID | doaj crossref |
| SourceType | Open Website Index Database |
| SubjectTerms | cascading style sheets (CSS) electronic mail (e-mail) java script short message service (SMS) uniform resource locator (URL) user behavior |
| Title | Multimodal framework for phishing attack detection and mitigation through behavior analysis using EM-BERT and SPCA-BASED EAI-SC-LSTM |
| URI | https://doaj.org/article/f116814a675b47d88df4915ff726ca52 |
| Volume | 6 |
| WOSCitedRecordID | wos001533300700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2673-530X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002784196 issn: 2673-530X databaseCode: DOA dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2673-530X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002784196 issn: 2673-530X databaseCode: M~E dateStart: 20200101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NT9swFLdQxYEdEGxMYwzkw26TR53YsX1sSxCT1qpai9Rb5E--1IC6wHEn_nCenRR1Jy5cIjlxIuv34uf37Pd-D6HvpnA8z0xGrM3BQVHgsxolAgmapYMmagxLxSbEZCIXCzXdKPUVY8JaeuAWuNNAaSEp02DYGiaclC4wRXkIIius5kn79oXacKZu18dpqmizZMALU6dhZZeR7zTjPykHFcDZfyvRBmF_WlnO99BuZxLiQTuUfbTl64_owwZR4Cf0nPJkl_cO-oV1OBUGexM_XLebSFg3jbZ32PkmBVfVWNcOL29aCg1odgV58DovH563dCQ4hr5f4XJMhuWfeXptNh0NyHAwK89wOfhFZiPyezYfH6DL83I-uiBd_QQCuPcbIhwVQhQBdJruGyVhvmolPc21KzQFxRY1DDWssDKETATed4b7wnsZOWCoyT-jXn1f-y8IM5k5S7VyIEcmjJOZZ8LLQK0wPOT8EP1YY1k9tDQZFbgXEfkqIV9F5KsO-UM0jHC_9owU1-kGCL7qBF-9Jfiv7_GRI7QTB5bib-U31GtWj_4Ybdun5ubv6iT9U3Ad_ytfAOH80Tc |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Multimodal+framework+for+phishing+attack+detection+and+mitigation+through+behavior+analysis+using+EM-BERT+and+SPCA-BASED+EAI-SC-LSTM&rft.jtitle=Frontiers+in+communications+and+networks&rft.au=Murhej%2C+Mahmoud&rft.au=Nallasivan%2C+G.&rft.date=2025-07-08&rft.issn=2673-530X&rft.eissn=2673-530X&rft.volume=6&rft_id=info:doi/10.3389%2Ffrcmn.2025.1587654&rft.externalDBID=n%2Fa&rft.externalDocID=10_3389_frcmn_2025_1587654 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2673-530X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2673-530X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2673-530X&client=summon |