View in EDS PDF Full Text

Improving Spam Intrusion Detection with the Machine Learning-Enhanced Chaotic Horse Ride Optimization Algorithm.

Saved in:
Bibliographic Details
Title: Improving Spam Intrusion Detection with the Machine Learning-Enhanced Chaotic Horse Ride Optimization Algorithm.
Authors: THANGARAJ, Amutha1 amudec25@gmail.com, SADAYAN, Geetha1 kasagee1971@gmail.com
Source: Technical Gazette / Tehnički Vjesnik. 2024, Vol. 31 Issue 6, p2072-2078. 7p.
Subjects: Optimization algorithms, Feature selection, Machine learning, Equestrianism, Metaheuristic algorithms, Spam email, Email security
Abstract: Automated spam detection, utilizing feature selection (FS) and machine learning (ML), categorizes and identifies unsolicited messages, like spam emails. The goal is to accurately differentiate and filter out spam, enhancing overall email security. This research presents the Chaotic Horse Ride Optimization Algorithm with Machine Learning-Driven Spam Detection and Classification (CHROA-MLSDC). CHROA-MLSDC efficiently classifies spam and non-spam through preprocessing and CHROA-based feature selection. It incorporates the Variation Auto Encoder (VAE) model and the Bat Algorithm (BA) for potential performance improvements. Simulations on Ling spam, Enron, Spam Assassin, and CSDM C2010 datasets demonstrate significant enhancements in precision, recall, accuracy, and execution speed compared to existing systems. CHROA-MLSDC achieves notable accuracy: 99.35% on Ling spam, 98.80% on Enron spam, 99.92% on Spam Assassin spam, and 98.02% on CSDM C2010 spam. Recall rates range from 97.82% to 98.05%. CHROA-MLSDC consistently outperforms similar methods, exhibiting accuracy from 96.91% to 97.55%. Execution time analysis reveals CHROA-MLSDC's consistently faster performance across all datasets. In summary, CHROA-MLSDC excels in spam detection, surpassing other methods across various evaluation metrics. [ABSTRACT FROM AUTHOR]
Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 181870328
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Improving Spam Intrusion Detection with the Machine Learning-Enhanced Chaotic Horse Ride Optimization Algorithm.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22THANGARAJ%2C+Amutha%22">THANGARAJ, Amutha</searchLink><relatesTo>1</relatesTo><i> amudec25@gmail.com</i><br /><searchLink fieldCode="AR" term="%22SADAYAN%2C+Geetha%22">SADAYAN, Geetha</searchLink><relatesTo>1</relatesTo><i> kasagee1971@gmail.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. 2024, Vol. 31 Issue 6, p2072-2078. 7p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Equestrianism%22">Equestrianism</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Spam+email%22">Spam email</searchLink><br /><searchLink fieldCode="DE" term="%22Email+security%22">Email security</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Automated spam detection, utilizing feature selection (FS) and machine learning (ML), categorizes and identifies unsolicited messages, like spam emails. The goal is to accurately differentiate and filter out spam, enhancing overall email security. This research presents the Chaotic Horse Ride Optimization Algorithm with Machine Learning-Driven Spam Detection and Classification (CHROA-MLSDC). CHROA-MLSDC efficiently classifies spam and non-spam through preprocessing and CHROA-based feature selection. It incorporates the Variation Auto Encoder (VAE) model and the Bat Algorithm (BA) for potential performance improvements. Simulations on Ling spam, Enron, Spam Assassin, and CSDM C2010 datasets demonstrate significant enhancements in precision, recall, accuracy, and execution speed compared to existing systems. CHROA-MLSDC achieves notable accuracy: 99.35% on Ling spam, 98.80% on Enron spam, 99.92% on Spam Assassin spam, and 98.02% on CSDM C2010 spam. Recall rates range from 97.82% to 98.05%. CHROA-MLSDC consistently outperforms similar methods, exhibiting accuracy from 96.91% to 97.55%. Execution time analysis reveals CHROA-MLSDC's consistently faster performance across all datasets. In summary, CHROA-MLSDC excels in spam detection, surpassing other methods across various evaluation metrics. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=181870328
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.17559/TV-20231011001016
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 7
        StartPage: 2072
    Subjects:
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Equestrianism
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Spam email
        Type: general
      – SubjectFull: Email security
        Type: general
    Titles:
      – TitleFull: Improving Spam Intrusion Detection with the Machine Learning-Enhanced Chaotic Horse Ride Optimization Algorithm.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: THANGARAJ, Amutha
      – PersonEntity:
          Name:
            NameFull: SADAYAN, Geetha
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 11
              Text: 2024
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 13303651
          Numbering:
            – Type: volume
              Value: 31
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: Technical Gazette / Tehnički Vjesnik
              Type: main
ResultId 1