Designing a novel framework of email spam detection using an improved heuristic algorithm and dual-scale feature fusion-based adaptive convolution neural network
Nowadays, e-mail is becoming more prevalent among individuals. In recent days, it has been declared to be the least expensive and quickest mode of communicating. In recent years, e-mail spam has become a big problem, so the number of e-mail spam has also increased, and they are used for unethical an...
Uložené v:
| Vydané v: | Information security journal. Ročník 34; číslo 4; s. 286 - 309 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Taylor & Francis
04.07.2025
|
| Predmet: | |
| ISSN: | 1939-3555, 1939-3547 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Nowadays, e-mail is becoming more prevalent among individuals. In recent days, it has been declared to be the least expensive and quickest mode of communicating. In recent years, e-mail spam has become a big problem, so the number of e-mail spam has also increased, and they are used for unethical and illegal conduct, scams, and theft. As a result, the main objective of the study is to detect e-mail spam using an adaptive deep-learning model with feature fusion. Initially, the input image and text are collected from benchmark datasets. Further, the raw text undergoes the pre-processing stage, and it is then followed by extracting the features using Bidirectional Encoder Representations from Transformers (BERT). Similarly, the Vision Transformer (ViT) is employed for extracting the features from raw images. Finally, these features are given in Dual Scale Feature Fusion-based Adaptive Convolutional Neural Network (DFF-ACNNet), and it is then fused for performing the classification in Convolutional Neural Network (CNN). Here, the hyper-parameters are tuned by using Modified Squid Game Optimizer (MSGO). Finally, the evaluation is done with various metrics to estimate the performance of the model and the accuracy of the developed system is 98.46, which is higher than the other conventional approaches. |
|---|---|
| AbstractList | Nowadays, e-mail is becoming more prevalent among individuals. In recent days, it has been declared to be the least expensive and quickest mode of communicating. In recent years, e-mail spam has become a big problem, so the number of e-mail spam has also increased, and they are used for unethical and illegal conduct, scams, and theft. As a result, the main objective of the study is to detect e-mail spam using an adaptive deep-learning model with feature fusion. Initially, the input image and text are collected from benchmark datasets. Further, the raw text undergoes the pre-processing stage, and it is then followed by extracting the features using Bidirectional Encoder Representations from Transformers (BERT). Similarly, the Vision Transformer (ViT) is employed for extracting the features from raw images. Finally, these features are given in Dual Scale Feature Fusion-based Adaptive Convolutional Neural Network (DFF-ACNNet), and it is then fused for performing the classification in Convolutional Neural Network (CNN). Here, the hyper-parameters are tuned by using Modified Squid Game Optimizer (MSGO). Finally, the evaluation is done with various metrics to estimate the performance of the model and the accuracy of the developed system is 98.46, which is higher than the other conventional approaches. |
| Author | Rohokale, Vandana M. Pingale, Subhash Biradar, Sangappa R. Bamane, Kalyan D. Kadam, Vikas S. |
| Author_xml | – sequence: 1 givenname: Vikas S. surname: Kadam fullname: Kadam, Vikas S. email: vikasskadam@gmail.com organization: Symbiosis Skills & Professional University – sequence: 2 givenname: Subhash surname: Pingale fullname: Pingale, Subhash organization: Sinhgad College of Engineering – sequence: 3 givenname: Sangappa R. surname: Biradar fullname: Biradar, Sangappa R. organization: SDMCET Dharwad – sequence: 4 givenname: Vandana M. surname: Rohokale fullname: Rohokale, Vandana M. organization: Sinhgad Institute of Technology and Science – sequence: 5 givenname: Kalyan D. surname: Bamane fullname: Bamane, Kalyan D. organization: D Y Patil College of Engineering Akurdi |
| BookMark | eNp9kNtKAzEURYMoWKufIOQHpk5unZk3xTsIvujzcJpLG80kJclU_Bz_1LRWH4XADknW5mSdoEMfvEbonNQzUrf1BelYx4QQM1pTPqOcUSraAzTZnldM8Obwby_EMTpJ6a2u55R09QR93ehkl976JQbsw0Y7bCIM-iPEdxwM1gNYh9MaBqx01jLb4PGYdu89tsM6FkbhlR6jTdlKDG4Zos2rodwrrEZwVZLgNDYa8hhLFjr4agGpcKBgne1GYxn8JrhxV-9LGbgSeTvFKToy4JI-2-cUvd7dvlw_VE_P94_XV0-VLL_NlVbzhglpWNsx1TVtqxtuYEHnjRS0aTlRQjaLjrVGEV5WKySnIEB0UhKuOZsi8dMrY0gpatOvox0gfvak7ree-1_P_dZzv_dcuMsfznoT4gBlZqf6DJ8uxKLSS5t69n_FN4MLixE |
| Cites_doi | 10.1109/ACCESS.2020.3030751 10.1016/j.optlastec.2023.109505 10.1007/s10207-023-00756-1 10.1155/2021/9210050 10.1007/s10586-017-1615-8 10.1109/ACCESS.2023.3310885 10.1016/j.procs.2022.03.087 10.1007/s12652-017-0621-2 10.1007/s41315-021-00217-9 10.1016/j.eij.2024.100473 10.1155/2023/6648970 10.1109/TIM.2021.3118090 10.1109/ICCV48922.2021.00676 10.1109/TCSI.2017.2757036 10.1007/s10207-019-00470-x 10.1080/1206212X.2023.2258328 10.1038/s41598-023-32465-z 10.1109/ACCESS.2019.2907000 10.1109/TII.2019.2895054 10.1109/ACCESS.2020.3017082 10.1007/s10994-006-9505-y 10.1007/s42452-019-0394-7 10.1007/s00521-017-3100-y 10.1109/ACCESS.2021.3116128 10.1016/j.eswa.2023.120977 10.3233/JCS-200111 10.1007/s00521-022-07148-x 10.1007/s11277-021-09221-5 10.1007/s11042-023-14814-2 10.1109/CTIT.2018.8649534 10.1109/ACCESS.2019.2944089 10.47852/bonviewJCCE2202192 10.1038/s41598-022-14338-z 10.1016/j.fcij.2018.11.006 10.1007/s41870-023-01516-z |
| ContentType | Journal Article |
| Copyright | 2024 Taylor & Francis Group, LLC 2024 |
| Copyright_xml | – notice: 2024 Taylor & Francis Group, LLC 2024 |
| DBID | AAYXX CITATION |
| DOI | 10.1080/19393555.2024.2432258 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1939-3547 |
| EndPage | 309 |
| ExternalDocumentID | 10_1080_19393555_2024_2432258 2432258 |
| Genre | Research Article |
| GroupedDBID | .4S .7F .DC .QJ 0BK 0R~ 30N 5VS 8VB AAENE AAGDL AAHIA AAJMT AALDU AAMIU AAPUL AAQRR ABCCY ABDBF ABFIM ABLIJ ABPAQ ABPEM ABTAI ABXUL ABXYU ACGFS ACTIO ADCVX ADGTB AEISY AEOZL AFRVT AGDLA AGMYJ AHDZW AIJEM AIYEW AKBVH AKOOK AKVCP ALMA_UNASSIGNED_HOLDINGS ALQZU AQRUH AQTUD AVBZW AWYRJ BLEHA CCCUG CE4 DGEBU DKSSO EAP EBR EBS EBU EHB EHE EJD EMI EST ESX E~A E~B FPAXQ GTTXZ H13 HF~ H~P J.P K60 K6~ KYCEM LJTGL M4Z NA5 NX~ PQBIZ QN7 QWB RIG RNANH ROSJB RTWRZ S-T SNACF TASJS TBQAZ TDBHL TEN TFL TFT TFW TNC TTHFI TUROJ TUS TWF UT5 UU3 ZGOLN ZL0 ~S~ AAYXX CITATION |
| ID | FETCH-LOGICAL-c258t-ed6735cf3893d9788e74fab267c527841d5c7b938fd14d1485c42a5a59cc14e43 |
| IEDL.DBID | TFW |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001369882600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1939-3555 |
| IngestDate | Sat Nov 29 07:49:55 EST 2025 Mon Oct 20 23:41:20 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c258t-ed6735cf3893d9788e74fab267c527841d5c7b938fd14d1485c42a5a59cc14e43 |
| PageCount | 24 |
| ParticipantIDs | informaworld_taylorfrancis_310_1080_19393555_2024_2432258 crossref_primary_10_1080_19393555_2024_2432258 |
| PublicationCentury | 2000 |
| PublicationDate | 07/04/2025 |
| PublicationDateYYYYMMDD | 2025-07-04 |
| PublicationDate_xml | – month: 07 year: 2025 text: 07/04/2025 day: 04 |
| PublicationDecade | 2020 |
| PublicationTitle | Information security journal. |
| PublicationYear | 2025 |
| Publisher | Taylor & Francis |
| Publisher_xml | – name: Taylor & Francis |
| References | e_1_3_3_30_1 Mani S. (e_1_3_3_23_1) 2023; 3 e_1_3_3_18_1 e_1_3_3_17_1 e_1_3_3_39_1 e_1_3_3_19_1 e_1_3_3_14_1 e_1_3_3_37_1 e_1_3_3_13_1 e_1_3_3_38_1 e_1_3_3_16_1 e_1_3_3_35_1 e_1_3_3_15_1 e_1_3_3_36_1 e_1_3_3_10_1 e_1_3_3_33_1 Liu T. (e_1_3_3_21_1) 2024; 3 e_1_3_3_34_1 e_1_3_3_31_1 e_1_3_3_11_1 e_1_3_3_32_1 e_1_3_3_40_1 Deepa D. (e_1_3_3_12_1) 2021; 12 e_1_3_3_7_1 e_1_3_3_6_1 e_1_3_3_9_1 e_1_3_3_8_1 e_1_3_3_29_1 e_1_3_3_28_1 e_1_3_3_25_1 e_1_3_3_24_1 e_1_3_3_27_1 e_1_3_3_26_1 e_1_3_3_3_1 e_1_3_3_2_1 e_1_3_3_20_1 e_1_3_3_5_1 e_1_3_3_4_1 e_1_3_3_22_1 |
| References_xml | – ident: e_1_3_3_14_1 doi: 10.1109/ACCESS.2020.3030751 – ident: e_1_3_3_30_1 doi: 10.1016/j.optlastec.2023.109505 – ident: e_1_3_3_2_1 doi: 10.1007/s10207-023-00756-1 – ident: e_1_3_3_39_1 doi: 10.1155/2021/9210050 – ident: e_1_3_3_20_1 doi: 10.1007/s10586-017-1615-8 – ident: e_1_3_3_4_1 doi: 10.1109/ACCESS.2023.3310885 – ident: e_1_3_3_25_1 doi: 10.1016/j.procs.2022.03.087 – ident: e_1_3_3_9_1 doi: 10.1007/s12652-017-0621-2 – volume: 3 start-page: 90 year: 2023 ident: e_1_3_3_23_1 article-title: Email spam detection using gated recurrent neural network publication-title: International Journal of Prograssive Research in Engineering Management and Science – ident: e_1_3_3_37_1 doi: 10.1007/s41315-021-00217-9 – ident: e_1_3_3_27_1 doi: 10.1016/j.eij.2024.100473 – ident: e_1_3_3_33_1 doi: 10.1007/s41315-021-00217-9 – ident: e_1_3_3_24_1 doi: 10.1155/2023/6648970 – ident: e_1_3_3_22_1 doi: 10.1109/TIM.2021.3118090 – ident: e_1_3_3_7_1 doi: 10.1109/ICCV48922.2021.00676 – ident: e_1_3_3_6_1 doi: 10.1109/TCSI.2017.2757036 – ident: e_1_3_3_35_1 doi: 10.1007/s10207-019-00470-x – ident: e_1_3_3_11_1 doi: 10.1080/1206212X.2023.2258328 – ident: e_1_3_3_8_1 doi: 10.1038/s41598-023-32465-z – ident: e_1_3_3_10_1 doi: 10.1109/ACCESS.2019.2907000 – ident: e_1_3_3_13_1 doi: 10.1109/TII.2019.2895054 – ident: e_1_3_3_18_1 doi: 10.1109/ACCESS.2020.3017082 – volume: 12 start-page: 1708 issue: 7 year: 2021 ident: e_1_3_3_12_1 article-title: Bidirectional encoder representations from transformers (BERT) language model for sentiment analysis task publication-title: Turkish Journal of Computer and Mathematics Education (TURCOMAT) – ident: e_1_3_3_29_1 doi: 10.1007/s10994-006-9505-y – ident: e_1_3_3_36_1 doi: 10.1007/s42452-019-0394-7 – ident: e_1_3_3_28_1 doi: 10.1007/s00521-017-3100-y – ident: e_1_3_3_19_1 doi: 10.1109/ACCESS.2021.3116128 – ident: e_1_3_3_40_1 doi: 10.1016/j.eswa.2023.120977 – ident: e_1_3_3_34_1 doi: 10.3233/JCS-200111 – ident: e_1_3_3_17_1 doi: 10.1007/s00521-022-07148-x – ident: e_1_3_3_31_1 doi: 10.1007/s11277-021-09221-5 – volume: 3 start-page: 6 issue: 3 year: 2024 ident: e_1_3_3_21_1 article-title: Spam detection and classification based on distilbert deep learning algorithm publication-title: Applied Science and Engineering Journal for Advanced Research – ident: e_1_3_3_32_1 doi: 10.1007/s11042-023-14814-2 – ident: e_1_3_3_16_1 doi: 10.1109/CTIT.2018.8649534 – ident: e_1_3_3_5_1 doi: 10.1109/ACCESS.2019.2944089 – ident: e_1_3_3_15_1 doi: 10.47852/bonviewJCCE2202192 – ident: e_1_3_3_3_1 doi: 10.1038/s41598-022-14338-z – ident: e_1_3_3_26_1 doi: 10.1016/j.fcij.2018.11.006 – ident: e_1_3_3_38_1 doi: 10.1007/s41870-023-01516-z |
| SSID | ssj0062190 |
| Score | 2.321576 |
| Snippet | Nowadays, e-mail is becoming more prevalent among individuals. In recent days, it has been declared to be the least expensive and quickest mode of... |
| SourceID | crossref informaworld |
| SourceType | Index Database Publisher |
| StartPage | 286 |
| SubjectTerms | Dual scale feature fusion-based adaptive convolutional neural network email spam detection modified squid game optimizer Vision Transformer |
| Title | Designing a novel framework of email spam detection using an improved heuristic algorithm and dual-scale feature fusion-based adaptive convolution neural network |
| URI | https://www.tandfonline.com/doi/abs/10.1080/19393555.2024.2432258 |
| Volume | 34 |
| WOSCitedRecordID | wos001369882600001&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: PRVAWR databaseName: Taylor & Francis Online Journals customDbUrl: eissn: 1939-3547 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062190 issn: 1939-3555 databaseCode: TFW dateStart: 20080324 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELYQYmChPMVbN7Aa8nAeHhFQMaCKgdcWOfaZVmrTqk37f_in-JxEggEWmJOLHN3T5-8-M3ZBs4x5mlseIqZcyNRy6SpTriQGGAaIyp_gvzxkg0H-9iYfWzThooVV0h7aNkQRPlaTc6ty0SHirlzNQazgidvdReIyEmSTNO7rKnsC9T31X7tYnDp_DJpzZclJpJvh-ekr37LTN-7SL1mn3_uH9W6zrbbkhOvGRnbYGla7rNdd5wCtd--xj1uP5nC5DBRU0xWOwXbQLZhawIkajcFFoAkYrD2EqwLCzbv3Kxj57gQaGOKyYX8GNX6fzkf1cOKeG6ChL75wJoFg0dOJgl1Sr45TJjWgjJpR7AUCwrcOAUS36dZeNWD1ffbcv3u6ueftDQ5cu1-sOZo0ixNtqSoybr-aYyasKqM004k_8TSJzkoZ59aEwpD-tIhUohKpdShQxAdsvZpWeMjAxSWtSiFkXsYiDbQU0gSlzVzIcoICj9hlp7li1hB1FGHLf9opoCAFFK0Cjpj8qt-i9h0S21xnUsS_yh7_QfaEbUZ0hzC1iMUpW6_nSzxjG3pVjxbzc2--nzKp754 |
| linkProvider | Taylor & Francis |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT9tAEF4BRaIXKLSoPFrmwHWDH-vHHqtCRNWQU4DcrPXubImUOChx8n_6T7uztqVwgEuRfLPHsjXv2W9mGLukXsY8zS0PEVMuZGq5dJEpVxIDDANE5U_wHwbZcJiPx3KzF4ZglZRD22ZQhLfVpNxUjO4gcVcu6KCx4IlL7yLRiwQJZb7NPtB2OkrARv3HzhqnTiOD5mRZcqLpunhee80L__RieumG3-kfvMcXf2L7bdQJPxoxOWRbWB2xg26jA7QK_pn9vfaADufOQEE1X-MUbIfegrkFnKnJFJwRmoHB2qO4KiDovHu-gokvUKCBJ1w1A6BBTf_MF5P6aebuG6C-L750UoFg0U8UBbuich0nZ2pAGfVM5hcIC9_qBNDETfftVYNX_8Lu-zejn7e8XeLAtfvFmqNJszjRlgIj41LWHDNhVRmlmU78oadJdFbKOLcmFO7KEy0ilahEah0KFPEx26nmFX5l4EyTVqUQMi9jkQZaCmmC0mbOajlCgSes17GueG5mdRRhOwK1Y0BBDChaBpwwucngovZFEttsNCniN2lP_4P2gu3dju4GxeDX8PcZ-xjRSmGqGItztlMvVviN7ep1PVkuvntZ_gdG6fPB |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT9wwELZaWlVcoC_Es51Dr6Z5OA8fEXRFVbTiQFtukWOPuyvtZle7Wf4P_5QZJ5Hg0F6KlFsyUaJ5evz5GyG-8FnGMi-9jBFzqXTupabKVBqNEcYRogk7-L-uivG4vL3V1z2acN3DKnkN7TuiiBCr2bmXzg-IuK9UczAreEaru0SdJoptsnwpXlHpnLFh34x-D8E4J4eMuo1lLVlmOMTzt9c8SU9PyEsfpZ3R7jN88Fux09eccNYZyTvxApv3YneY5wC9e38Q9xcBzkHJDAw0izucgR-wW7DwgHMznQGFoDk4bAOGqwEGztPzDUxDewIdTHDT0T-Dmf1ZrKbtZE73HfCpL7kmm0DwGPhEwW-4WSc5lTowziw5-AIj4XuPAObbpG9vOrT6R_Fz9O3m_FL2IxykpV9sJbq8SDPruSxytGAtsVDe1Ele2CxsebrMFrVOS-9iRVeZWZWYzGTa2lihSvfEVrNocF8ABSZraqV0Wacqj6xW2kW1LyhmkaDCA3E6aK5adkwdVdwToA4KqFgBVa-AA6Ef67dqQ4vEd_NMqvSfsof_IftZvLm-GFVX38c_jsR2wvOEuV2sjsVWu9rgiXht79rpevUpWPIDdrPycw |
| 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=Designing+a+novel+framework+of+email+spam+detection+using+an+improved+heuristic+algorithm+and+dual-scale+feature+fusion-based+adaptive+convolution+neural+network&rft.jtitle=Information+security+journal.&rft.au=Kadam%2C+Vikas+S.&rft.au=Pingale%2C+Subhash&rft.au=Biradar%2C+Sangappa+R.&rft.au=Rohokale%2C+Vandana+M.&rft.date=2025-07-04&rft.issn=1939-3555&rft.eissn=1939-3547&rft.volume=34&rft.issue=4&rft.spage=286&rft.epage=309&rft_id=info:doi/10.1080%2F19393555.2024.2432258&rft.externalDBID=n%2Fa&rft.externalDocID=10_1080_19393555_2024_2432258 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1939-3555&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1939-3555&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1939-3555&client=summon |